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data/SceneVerse/hm3d_categories.json

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"banner", "bar", "bar cabinet", "bar chair", "bar soap", "barbecue", "barbell", "barrel", "base", "baseball bat", "baseball cap", "baseboard", "basin", "basin faucet", "basket", "basket of fruits", "basket of something", "basket of towels", "basket with books", "basket with clothes", "basketball hoop", "basketballs", "baskets", "bath", "bath bar", "bath cabinet", "bath carpet", "bath cosmetics", "bath curtain", "bath curtain bar", "bath dial", "bath door", "bath door frame", "bath faucet", "bath floor", "bath grab bar", "bath hanger", "bath mat", "bath shelf", "bath shower", "bath side table", "bath sink", "bath tap", "bath towel", "bath towels", "bath tub", "bath utensil", "bath wall", "bathmat", "bathrobe", "bathroom accessory", "bathroom art", "bathroom cabinet", "bathroom cabinet door", "bathroom cabinet drawer", "bathroom counter", "bathroom floor", "bathroom glass", "bathroom mat", "bathroom rug", "bathroom shelf", "bathroom stuff", "bathroom towel", "bathroom utensil", "bathroom utensils", "bathroom wall", "bathroom window", "bathtub", "bathtub knob", "bathtub platform", "bathtub tap", "bathtub utensil", "beam", "bean bag chair", "beanbag", "beanbag chair", "bed", "bed base", "bed cabinet", "bed cabinet lamp", "bed comforter", "bed curtain", "bed for pet", "bed ladder", "bed light", "bed sheet", "bed small", "bed stand", "bed table", "bedding", "bedframe", "bedpost", "bedroom ceiling", "bedroom table", "bedside cabinet", "bedside cabinet door", "bedside cabinet drawer", "bedside lamp", "bedside table", "beer crate", "bell", "belt", "bench", "beverage dispenser", "bicycle", "bicycle helmet", "bicycle helmets", "bidet", "big bag", "billiard balls", "billiard cue", "billiard cues", "bin", "binder", "binders", "birdhouse", "blackboard", "blanket", "blanket basket", "blankets", "blinds", "blouse", "board", "board game", "board games", "board with keys", "boards", "boat model", "boiler", "bonsai tree", "book", "book cabinet", "book display", "book rack", "bookshelf", "bookstand", "boots", "bottle", "bottle dispenser", "bottle of detergent", "bottle of soap", "bottle of water", "bottle of wine", "bottle opener", "bottles", "bottles of water", "bottles of wine", "bottom of stairs", "bouquet", "bowl", "bowl of fruit", "bowl of fruits", "bowl of sweets", "bowl with sweets", "bowls", "box", "box of food", "box of fruit", "box of paper", "box of something", "box of tissue", "box of tissues", "box pen", "box with books", "box with jewelry", "box with photos", "box with shoes", "box with tea", "box with toys", "boxes", "boxes with books", "boxing ring", "bread", "bread bin", "bread box", "breadbox", "bricks", "bridge", "briefcase", "brochure", "brochures", "broom", "broomstick", "brush", "brushes", "bucket", "buckets", "buffet", "bulletin board", "bunk bed", "bureau", "bust", "button", "cabinet", "cabinet /otherroom", "cabinet /w clutter", "cabinet /w cluttered art", "cabinet clutter", "cabinet counter", "cabinet door", "cabinet drawer", "cabinet kitchen", "cabinet mirror", "cabinet parts", "cabinet table", "cable", "cables", "cage", "cake", "calculator", "calendar", "camera", "camping chair", "can", "can of paint", "canal", "candelabra", "candle", "candle holder", "candle stand", "candlestick", "canister", "canoe", "canopy", "cans", "cans of paint", "canvas", "cap", "car", "car model", "card", "cardboard", "cardboard box", "carpet", "carpet roll", "cart", "case", "cases", "cash register", "casket", "cassette", "cat", "cat bed", "cat food", "cat food bag", "cat litter box", "cat toilet", "cat tree", "cd", "cd player", "cds", "ceiling arch", "ceiling bedroom", "ceiling boarder", "ceiling border", "ceiling chassis", "ceiling corridor", "ceiling decorative lamp", "ceiling dome", "ceiling door", "ceiling duct", "ceiling fan", "ceiling fan lamp", "ceiling fan vent", "ceiling fire detector", "ceiling fixture", "ceiling floor", "ceiling hanger", "ceiling ladder", "ceiling lamp", "ceiling lamp hanger", "ceiling lamp rail", "ceiling light", "ceiling light fixture connection", "ceiling lower", "ceiling molding", "ceiling panel", "ceiling pipe", "ceiling support", "ceiling under staircase", "ceiling under stairs", "ceiling vent", "ceiling wall", "ceiling window", "ceiling/west wall", "central heating furnace", "ceramics", "chain", "chair", "chair /w clutter", "chair stand", "chaise", "chaise longue", "chamber pot", "chandelier", "changing table", "charger", "chassis", "chess", "chest", "chest bench", "chest drawer", "chest of drawer", "chest of drawers", "child car seat", "chimney", "christmas tree", "circular sofa", "clarinet", "cleaner", "cleaner bottle", "cleaner brush", "cleaning clutter", "cleaning fluid", "cleaning liquid", "cleaning paper", "cleaning paste", "cleaning powders/ liquids", "cleaning sponge", "cleaning spray", "clock", "closet", "closet area for hanging clothes", "closet door", "closet floor", "closet mirror wall", "closet rod", "closet shelf", "closet shelving", "closet storage area", "cloth", "cloth dryer", "cloth hanger", "cloth hangers", "cloth holder", "clothes", "clothes bag", "clothes container", "clothes dryer", "clothes hamper", "clothes hanger", "clothes hanger rod", "clothes on shelf", "clothes rack", "clothing stand", "clutter", "coach", "coaster", "coat", "coat hanger", "coat rack", "coffee machine", "coffee maker", "coffee mug", "coffee table", "column", "compound wall", "compressor", "computer", "computer chair", "computer desk", "computer equipment", "computer mouse", "computer tower", "condensation furnace", "condiment", "cone", "conference phone", "console", "console pad", "console pad charger", "construction stuff", "container", "containers", "control", "control panel", "controller", "cook book", "cooker", "cooker hood", "cookies", "copier", "copier machine", "cork board", "cornice", "cosmetic", "cosmetics", "couch", "counter", "counter desk", "counter door", "countertop", "countertop /otherroom", "countertop item", "cover", "cradle", "crate", "crayon", "crib", "cross", "cross-trainer", "crutches", "cuddly toy", "cup", "cups", "curb", "curtain", "curtain bar", "curtain box", "curtain hanger", "curtain rail", "curtain rod", "curtain rod cover", "curtain valence", "cushion", "cutlery", "cutting board", "cyp", "dartboard", "deck chair", "decoder", "decor", "decoration", "decorative bottle", "decorative bowl", "decorative ceramic", "decorative cloth", "decorative dinnerware", "decorative frame", "decorative lamp", "decorative lantern", "decorative mask", "decorative plant", "decorative plate", "decorative quilt", "decorative tray", "decorative vase", "decorative vessel", "decorative window", "dehumidifier", "den", "desk", "desk cabinet", "desk chair", "desk clutter", "desk door", "desk lamp", "desk organizer", "detergent", "detergent bottle", "device", "dining chair", "dining table", "dinner chair", "dinner table", "dinnerware", "diploma", "dirt ground", "dish", "dish cabinet", "dish dryer", "dish rack", "dishes", "dishrag", "dishwasher", "dispenser", "display", "display cabinet", "display case", "display of pictures", "display table", "document", "document holder", "dog bed", "dog leash", "dog toy", "doily", "doll", "door /otherroom", "door cabinet", "door frame", "door handle", "door hinge", "door knob", "door screen", "door stopper", "door window", "door/window", "door/window frame", "doorbell", "doormat", "doorpost", "doorstep", "doorway", "double armchair", "drain", "drainage", "drainpipe", "draw", "drawer", "drawer cabinet", "drawer cart", "drawer desk", "drawer sink table", "drawers", "drawers for clothes", "drawing", "dress", "dresser", "dressing table", "dried flowers", "drill", "drinking fountain", "drum", "drywall board", "duct", "dumbbell", "dustbin", "dustpan", "duvet", "dvd", "dvd player", "easel", "easy chair", "electric box", "electric cable", "electric cord", "electric device", "electric drum", "electric freshener", "electric guitar", "electric heater", "electric hub", "electric installation", "electric kettle", "electric outlet", "electric percussion", "electric plug", "electric toothbrush", "electric wire", "electric wire casing", "electrical box", "electrical controller", "electrical device", "electrical installation", "electrical switchboard", "electricity box", "electronics", "elephant sculpture", "elevator", "elevator door", "emergency sign", "end table", "entertainment set", "entrance arch", "entry phone", "exercise ball", "exercise bike", "exercise equipment", "exercise ladder", "exercise machine", "exercise mat", "exercise mat roll", "exercising blocks", "exhaust pipe", "exhibition panel", "exhibition picture", "exhibition table", "exhibition window", "exhibition window frame", "exit sign", "extension cord", "extension lead", "extractor", "extractor hood", "eyeglasses", "fan", "fan air vent", "fan coil", "faucet", "fence", "figure", "figurine", "file", "file binder", "file cabinet", "fire alarm", "fire detector", "fire extinguisher", "fire pit", "fire screen", "fire sprinkler", "firebox", "fireplace", "fireplace brush", "fireplace floor", "fireplace mirror", "fireplace sconce", "fireplace shelf", "fireplace tool set", "fireplace utensil", "fireplace wall", "firewood", "firewood bag", "firewood chest", "firewood holder", "fish tank", "fishing pole", "fishing rod", "fitness ball", "flag", "flashlight", "flatware", "floor /otherroom", "floor /outside", "floor frame", "floor lamp", "floor mat", "floor stand", "floor vent", "flower", "flower stand", "flower vase", "flowerbed", "flowerpot", "fluorescent light", "flush", "flush button", "flush push", "foam", "folded chair", "folded table", "folder", "folding chair", "folding stand", "food", "food processor", "food stand", "food tray", "foosball game table", "foosball table", "foot spa", "foot stand", "football", "footrest", "footstool", "fork", "frame", "framed text", "freezer", "fridge", "fruit", "fruit bowl", "frying pan", "fume cupboard", "fur", "fur carpet", "furnace", "furniture", "fuse box", "fuse panel", "game", "game board", "game console", "gap", "garage door", "garage door frame", "garage door motor", "garage door opener", "garage door opener bar", "garage door opener motor", "garage door opener railing", "garage door railing", "garage light", "garden bench", "garden chair", "garden deck", "gas box", "gas container", "gas furnace", "gas meter", "gate", "gauge", "gift", "glass", "glass container", "glass door", "glass pane", "glasses", "globe", "globe stand", "gloves", "glue", "goalpost", "golf sticks", "grab bar", "gramophone", "grandfather clock", "grass", "grate", "gravel", "grill", "groceries", "guitar", "guitar case", "guitar case cover", "guitar cases", "guitar frame", "guitar pedals", "guitar stand", "guitar straps", "gun", "gutter", "gym equipment", "gym mat", "gym rope", "gym stepper", "hair brush", "hair dryer", "hammer", "hammock", "hand cloth", "hand dryer", "hand soap", "hand towel", "handbag", "handcart", "handkerchiefs", "handle", "handrail", "hanger", "hanging clothes", "hat", "hatch", "hats", "headboard", "headphones", "headset", "hearth", "heater", "heater piping", "hi-hat", "hi-hat stand", "high shelf", "highchair", "holder", "hole puncher", "holy cross", "hook", "hose", "hot water/cold water knob", "hourglass", "hoverboard", "hunting trophy", "hutch", "ice maker", "icebox", "identifier", "information", "installation", "instrument", "iron", "iron board", "ironing board", "island", "jacket", "jacuzzi", "jar", "jars", "jewelry", "jewelry box", "jug", "junk", "keg", "kettle", "keyboard", "keyboard box", "keyboard cover", "keyboard piano", "keyboard stand", "keys", "kitchen appliance", "kitchen cabinet", "kitchen cabinet door", "kitchen cabinet drawer", "kitchen cabinet lower", "kitchen ceiling", "kitchen chair", "kitchen counter", "kitchen counter item", "kitchen counter support", "kitchen countertop item", "kitchen countertop items", "kitchen decoration", "kitchen extractor", "kitchen gloves", "kitchen handle", "kitchen island", "kitchen knife set", "kitchen lower cabinet", "kitchen lower shelf", "kitchen seating", "kitchen shelf", "kitchen sink", "kitchen sink cabinet", "kitchen table", "kitchen top", "kitchen towel", "kitchen utensil", "kitchen utensils", "kitchen wall", "kitchenware", "knife", "knife holder", "knife set", "knife stand", "knob", "l-shaped sofa", "lace doily", "ladder", "lamp", "lamp shade", "lamp stand", "lamp table", "lampshade", "landing", "lantern", "laptop", "laundry", "laundry bag", "laundry basket", "laundry machine", "lawn", "lawn mower", "leaflets", "led tv", "ledge", "leg rest", "level", "lid", "light", "light fixture", "light switch", "lighter", "lighting fixture", "liquid", "liquid cleaner", "liquid container", "liquid soap", "locker", "lounge chair", "lounger", "lower cabinet", "luggage", "machine", "magazine", "magazine rack", "magazines", "magic marker", "magic marker box", "mail", "mailbox", "makeup accessories", "mannequin", "mantel", "mantle", "map", "mascot", "mascots", "massage bed", "mat", "material", "measuring tape", "medal", "medal collection", "media console", "medical lamp", "medical object", "menu", "menu board", "meshwork", "meter", "microphone", "microphone accessory", "microwave", "mini fridge", "mirror", "mirror /otherroom", "mirror door", "mirror frame", "mixer", "mobile", "model", "modem", "molding", "monitor", "moose head/sculpture/hunting trophy", "mop", "mortar", "motion detector", "motorcycle", "mouse", "mousepad", "mug", "music album shelf", "music equipment", "music equipment stand", "music player", "napkin", "napkins", "newspaper", "newspaper basket", "niche", "night lamp", "nightstand", "note", "note board", "notebook", "notebooks", "notes", "oar", "object", "objects", "office chair", "office table", "office wall", "oil lamp", "ornament", "ottoman", "outlet", "oven", "oven and stove", "oven vent", "overhang", "package", "pad", "painting", "painting /otherroom", "painting frame", "painting roll", "painting rolls", 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"water outlet", "water pump", "water tank", "watering can", "weight", "weight bench", "weights", "wheel", "wheelbarrow", "whine shelf", "whiteboard", "wifi router", "window /otherroom", "window /outside", "window curtain", "window frame /otherroom", "window glass", "window seat", "window shade", "window shutter", "window shutters", "window valence", "window/door", "wine", "wine bottle", "wine cabinet", "wine rack", "wine storage", "wire", "wood", "workout bike", "workstation", "worktop", "wreath", "wrench", "yoga mat", "window frame"]

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data/scene_datasets/3RScan/download_3rscan.py

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+#!/usr/bin/env python
+# Downloads 3RScan public data release
+
+# The data is released under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License.
+# Some useful info: Each scan is identified by a unique ID listed here:
+# http://campar.in.tum.de/public_datasets/3RScan/scans.txt or https://github.com/WaldJohannaU/3RScan/tree/master/splits
+#
+# Script usage:
+# - To download the entire 3RScan release: download.py -o [directory in which to download]
+# - To download a specific scan (e.g., 19eda6f4-55aa-29a0-8893-8eac3a4d8193): download.py -o [directory in which to download] --id 19eda6f4-55aa-29a0-8893-8eac3a4d8193
+# - To download the tfrecords: download.py -o [directory in which to download] --type=tfrecords
+# - The corresponding metadata file is here: http://campar.in.tum.de/public_datasets/3RScan/3RScan.json
+# - 3D semantic scene graphs for 3RScan are available for download on our project page https://3dssg.github.io
+
+import sys
+import argparse
+import os
+
+if sys.version_info.major >= 3 and sys.version_info.minor >= 6:
+    import urllib.request as urllib
+else:
+    import urllib
+import tempfile
+import re
+
+BASE_URL = 'http://campar.in.tum.de/public_datasets/3RScan/'
+DATA_URL = BASE_URL + 'Dataset/'
+TOS_URL = 'http://campar.in.tum.de/public_datasets/3RScan/3RScanTOU.pdf'
+TEST_FILETYPES = ['mesh.refined.v2.obj', 'mesh.refined.mtl', 'mesh.refined_0.png', 'sequence.zip']
+# We only provide semantic annotations for the train and validation scans as well as the for the
+# reference scans in the test set.
+FILETYPES = TEST_FILETYPES + ['labels.instances.annotated.v2.ply', 'mesh.refined.0.010000.segs.v2.json', 'semseg.v2.json']
+
+RELEASE = 'release_scans.txt'
+HIDDEN_RELEASE = 'test_rescans.txt'
+
+RELEASE_SIZE = '~94GB'
+id_reg = re.compile(r"[a-z0-9]{8}-[a-z0-9]{4}-[a-z0-9]{4}-[a-z0-9]{4}-[a-z0-9]{12}")
+
+def get_scans(scan_file):
+    scan_lines = urllib.urlopen(scan_file)
+    scans = []
+    for scan_line in scan_lines:
+        scan_line = scan_line.decode('utf8').rstrip('\n')
+        match = id_reg.search(scan_line)
+        if match:
+            scan_id = match.group()
+            scans.append(scan_id)
+    return scans
+
+def download_release(release_scans, out_dir, file_types):
+    print('Downloading 3RScan release to ' + out_dir + '...')
+    for scan_id in release_scans:
+        scan_out_dir = os.path.join(out_dir, scan_id)
+        download_scan(scan_id, scan_out_dir, file_types)
+    print('Downloaded 3RScan release.')
+
+def download_file(url, out_file):
+    print(url)
+    out_dir = os.path.dirname(out_file)
+    if not os.path.isdir(out_dir):
+        os.makedirs(out_dir)
+    if not os.path.isfile(out_file):
+        print('\t' + url + ' > ' + out_file)
+        fh, out_file_tmp = tempfile.mkstemp(dir=out_dir)
+        f = os.fdopen(fh, 'w')
+        f.close()
+        urllib.urlretrieve(url, out_file_tmp) 
+        os.rename(out_file_tmp, out_file)
+    else:
+        print('WARNING: skipping download of existing file ' + out_file)
+
+def download_scan(scan_id, out_dir, file_types):
+    print('Downloading 3RScan scan ' + scan_id + ' ...')
+    if not os.path.isdir(out_dir):
+        os.makedirs(out_dir)
+    for ft in file_types:
+        url = DATA_URL + '/' + scan_id + '/' + ft
+        out_file = out_dir + '/' + ft
+        download_file(url, out_file)
+    print('Downloaded scan ' + scan_id)
+
+def download_tfrecord(url, out_dir, file):
+    if not os.path.isdir(out_dir):
+        os.makedirs(out_dir)
+    out_file = os.path.join(out_dir, file)
+    download_file(url + '/' + file, out_file)
+
+
+def main():
+    parser = argparse.ArgumentParser(description='Downloads 3RScan public data release.')
+    parser.add_argument('-o', '--out_dir', required=True, help='directory in which to download')
+    parser.add_argument('--id', help='specific scan id to download')
+    parser.add_argument('--type', help='specific file type to download')
+    args = parser.parse_args()
+
+    print('By pressing any key to continue you confirm that you have agreed to the 3RScan terms of use as described at:')
+    print(TOS_URL)
+    print('***')
+    print('Press any key to continue, or CTRL-C to exit.')
+
+    release_scans = get_scans(BASE_URL + RELEASE)
+    test_scans = get_scans(BASE_URL + HIDDEN_RELEASE)
+    file_types = FILETYPES;
+    file_types_test = TEST_FILETYPES;
+
+    if args.type:  # download file type
+        file_type = args.type
+        if file_type == 'tfrecords':
+            download_tfrecord(BASE_URL, args.out_dir, 'val-scans.tfrecords')
+            download_tfrecord(BASE_URL, args.out_dir, 'train-scans.tfrecords')
+            return
+        elif file_type not in FILETYPES:
+            print('ERROR: Invalid file type: ' + file_type)
+            return
+        file_types = [file_type]
+        if file_type not in TEST_FILETYPES:
+            file_types_test = []
+        else:
+            file_types_test = [file_type]
+    if args.id:  # download single scan
+        scan_id = args.id
+        if scan_id not in release_scans and scan_id not in test_scans:
+            print('ERROR: Invalid scan id: ' + scan_id)
+        else:
+            out_dir = os.path.join(args.out_dir, scan_id)
+
+            if scan_id in release_scans:
+                download_scan(scan_id, out_dir, file_types)
+            elif scan_id in test_scans:
+                download_scan(scan_id, out_dir, file_types_test)
+    else: # download entire release
+        if len(file_types) == len(FILETYPES):
+            print('WARNING: You are downloading the entire 3RScan release which requires ' + RELEASE_SIZE + ' of space.')
+        else:
+            print('WARNING: You are downloading all 3RScan scans of type ' + file_types[0])
+        print('Note that existing scan directories will be skipped. Delete partially downloaded directories to re-download.')
+        print('***')
+        print('Press any key to continue, or CTRL-C to exit.')
+        key = input('')
+        download_release(release_scans, args.out_dir, file_types)
+        download_release(test_scans, args.out_dir, file_types_test)
+
+if __name__ == "__main__": main()

data/scene_datasets/3RScan/generate_pcd.py

@@ -0,0 +1,122 @@
+import open3d as o3d
+import matplotlib.pyplot as plt
+import numpy as np
+import os
+import math
+import torch
+from PIL import Image
+import random
+from einops import einsum
+
+
+@torch.no_grad()
+def get_frustum_mask(points, H, W, intrinsics, view_matrices, near = 0.02, far = 10.):
+
+    ones = torch.ones_like(points[:, 0]).unsqueeze(-1)
+    homo_points = torch.cat([points, ones], dim=-1)
+
+    view_points = einsum(view_matrices, homo_points, "b c, N c -> N b")
+    view_points = view_points[:, :3]
+
+    uv_points = einsum(intrinsics, view_points, "b c, N c -> N b")
+
+    z = uv_points[:, -1:]
+    uv_points = uv_points[:, :2] / z
+    u, v = uv_points[:, 0], uv_points[:, 1]
+    depth = view_points[:, -1]
+    
+    cull_near_fars = (depth >= near) & (depth <= far)
+
+    mask = cull_near_fars & (u >= 0) & (u <= W-1) & (v >= 0) & (v <= H-1)
+    return mask
+
+
+def config_parser():
+
+    import configargparse
+    parser = configargparse.ArgumentParser()
+
+
+    # training options
+    parser.add_argument("--near", type=float, default=0., 
+                        help='near distance')
+    parser.add_argument("--far", type=float, default=10., 
+                        help='far distance')
+    parser.add_argument("--camera_height", type=int, default=24, 
+                        help='height of the feature map')
+    parser.add_argument("--camera_width", type=int, default=24, 
+                        help='width of the feature map')
+    parser.add_argument("--feature_fields_search_radius", type=float, default=1., 
+                        help='search radius for near features')
+    parser.add_argument("--feature_fields_search_num", type=int, default=4, 
+                        help='The number of searched near features')
+    parser.add_argument("--mlp_net_layers", type=int, default=8, 
+                        help='layers in mlp network')
+    parser.add_argument("--mlp_net_width", type=int, default=768, 
+                        help='channels per layer in mlp net')
+
+    # rendering options
+    parser.add_argument("--N_samples", type=int, default=512, 
+                        help='number of coarse samples per ray')
+    parser.add_argument("--N_importance", type=int, default=16,
+                        help='number of fine samples per ray')
+
+    return parser
+
+
+parser = config_parser()
+args, unknown = parser.parse_known_args() #parser.parse_args()  
+
+
+scene_list = os.listdir('3RScan')
+image_list = []
+for scene_id in scene_list:
+    for image_id in range(1000):  
+        image_path = '3RScan/'+scene_id+'/sequence/frame-'+str(image_id).zfill(6)+'.color.jpg'
+        if not os.path.exists(image_path):
+            break
+        image_list.append('3RScan/'+scene_id+'/sequence/frame-'+str(image_id).zfill(6))
+
+    random.shuffle(image_list)
+    image_list = image_list[:30]
+    #image_list = random.sample(image_list,min(30,len(image_list)))
+
+    pcd_all = o3d.geometry.PointCloud() 
+    for image_path in image_list:
+        intrinsic = np.eye(4)
+        with open('3RScan/'+scene_id+'/sequence/_info.txt', 'r') as file:  
+            intrinsic_raw = [line.strip() for line in file]
+        intrinsic_raw = intrinsic_raw[9].split(" ")[2:]
+
+        for i in range(4):  
+            for j in range(4): 
+                intrinsic[i][j] = float(intrinsic_raw[i*4+j])
+
+        extrinsic = np.eye(4)
+        with open(image_path+'.pose.txt', 'r') as file:  
+            extrinsic_raw = [line.strip() for line in file]
+        for i in range(4):  
+            for j in range(4): 
+                extrinsic[i][j] = float(extrinsic_raw[i].split()[j])
+
+        R = extrinsic[:3,:3]
+        T = extrinsic[:3,3:4]
+
+        color_raw = o3d.io.read_image(image_path + ".color.jpg")
+        depth_raw = o3d.geometry.Image(np.asarray(Image.open(image_path + ".depth.pgm")).astype(np.uint16))
+        #rgbd_image = o3d.geometry.RGBDImage.create_from_color_and_depth(color_raw, depth_raw, depth_scale=1000.0, depth_trunc=1000.0, convert_rgb_to_intensity=False)
+
+        #pcd = o3d.geometry.PointCloud.create_from_rgbd_image(rgbd_image,o3d.camera.PinholeCameraIntrinsic(224,172,intrinsic[0][0],intrinsic[1][1],intrinsic[0][2],intrinsic[1][2]))
+        pcd = o3d.geometry.PointCloud.create_from_depth_image(depth_raw, o3d.camera.PinholeCameraIntrinsic(224,172,intrinsic[0][0],intrinsic[1][1],intrinsic[0][2],intrinsic[1][2]), depth_scale=1000.0, depth_trunc=1000.0)
+
+        points = np.asarray(pcd.points)
+        
+        points = (R @ points.T + T).T
+        pcd.points = o3d.utility.Vector3dVector(points)
+        pcd_all += pcd
+        #o3d.visualization.draw_geometries([pcd_all])
+        #mask = get_frustum_mask(torch.tensor(np.array(pcd_all.points)), 240, 320, intrinsic[:3,:3], np.linalg.inv(extrinsic), near = 0.02, far = 10.)
+        
+    
+    
+    o3d.visualization.draw_geometries([pcd_all])

data/scene_datasets/3RScan/process_3RScan.py

@@ -0,0 +1,12 @@
+import os
+import zipfile
+from tqdm import tqdm
+
+
+dir_list = os.listdir("3RScan")
+for dir in tqdm(dir_list):
+    source_file = os.path.join("3RScan",dir,"sequence.zip")
+    target_dir = os.path.join("3RScan",dir,"sequence")
+    with zipfile.ZipFile(source_file,'r') as zip_ref:
+        zip_ref.extractall(target_dir)
+    os.remove(source_file)

data/scene_datasets/ARKitScenes/arkitscenes.py

@@ -0,0 +1,127 @@
+import json
+from glob import glob
+from omegaconf import OmegaConf
+from joblib import Parallel, delayed, parallel_backend
+
+import torch
+import numpy as np
+import trimesh
+from tqdm import tqdm
+from scipy.spatial.transform import Rotation
+
+from preprocess.build import ProcessorBase
+from preprocess.utils.label_convert import ARKITSCENE_SCANNET as label_convert
+from preprocess.utils.align_utils import compute_box_3d, calc_align_matrix, rotate_z_axis_by_degrees
+from preprocess.utils.constant import *
+
+
+class ARKitScenesProcessor(ProcessorBase):
+    def record_splits(self, scan_ids):
+        split_dir = self.save_root / 'split'
+        split_dir.mkdir(exist_ok=True)
+        if (split_dir / 'train_split.txt').exists() and (split_dir / 'val_split.txt').exists():
+            return
+        split = {
+            'train': [],
+            'val': []}
+        split['train'] = [scan_id[1] for scan_id in scan_ids if scan_id[0] == 'Training']
+        split['val'] = [scan_id[1] for scan_id in scan_ids if scan_id[0] == 'Validation']
+        for _s, _c in split.items():
+            with open(split_dir / f'{_s}_split.txt', 'w', encoding='utf-8') as fp:
+                fp.write('\n'.join(_c))
+
+    def read_all_scans(self):
+        scan_ids = []
+        for split in ['Training', 'Validation']:
+            scan_paths = glob(str(self.data_root) + f'/{split}/*')
+            scan_ids.extend([(split, path.split('/')[-1]) for path in scan_paths])
+        return scan_ids
+
+    def process_point_cloud(self, scan_id, plydata, annotations):
+        vertices = plydata.vertices
+        vertex_colors = plydata.visual.vertex_colors
+        vertex_colors = vertex_colors[:, :3]
+
+        vertex_instance = np.zeros((vertices.shape[0]))
+        inst_to_label = {}
+        bbox_list = []
+
+        for _i, label_info in enumerate(annotations["data"]):
+            obj_label = label_info["label"]
+            object_id = _i + 1
+            rotation = np.array(label_info["segments"]["obbAligned"]["normalizedAxes"]).reshape(3, 3)
+            r = Rotation.from_matrix(rotation)
+
+            transform = np.array(label_info["segments"]["obbAligned"]["centroid"]).reshape(-1, 3)
+            scale = np.array(label_info["segments"]["obbAligned"]["axesLengths"]).reshape(-1, 3)
+            trns = np.eye(4)
+            trns[0:3, 3] = transform
+            trns[0:3, 0:3] = rotation.T
+            box_trimesh_fmt = trimesh.creation.box(scale.reshape(3,), trns)
+            obj_containment = np.argwhere(box_trimesh_fmt.contains(vertices))
+
+            vertex_instance[obj_containment] = object_id
+            inst_to_label[object_id] = label_convert[obj_label]
+
+            box3d = compute_box_3d(scale.reshape(3).tolist(), transform, rotation)
+            bbox_list.append(box3d)
+        if len(bbox_list) == 0:
+            return
+
+        #align_angle = calc_align_matrix(bbox_list)
+        vertices = np.array(vertices)
+        #vertices = rotate_z_axis_by_degrees(np.array(vertices), align_angle)
+        if np.max(vertex_colors) <= 1:
+            vertex_colors = vertex_colors * 255.0
+        #center_points = np.mean(vertices, axis=0)
+        #center_points[2] = np.min(vertices[:, 2])
+        #vertices = vertices - center_points
+
+        assert vertex_colors.shape == vertices.shape
+        assert vertex_colors.shape[0] == vertex_instance.shape[0]
+
+        if self.check_key(self.output.pcd):
+            torch.save(inst_to_label, self.inst2label_path / f"{scan_id}.pth")
+            torch.save((vertices, vertex_colors, vertex_instance), self.pcd_path / f"{scan_id}.pth")
+            #np.save(self.pcd_path / f"{scan_id}_align_angle.npy", align_angle)
+
+    def scene_proc(self, scan_id):
+        split = scan_id[0]
+        scan_id = scan_id[1]
+        data_root = self.data_root / split / scan_id
+        
+        if not (data_root / f'{scan_id}_3dod_mesh.ply').exists():
+            return
+        if not (data_root / f'{scan_id}_3dod_annotation.json').exists():
+            return
+
+        plydata = trimesh.load(data_root / f'{scan_id}_3dod_mesh.ply', process=False)
+        with open((data_root / f'{scan_id}_3dod_annotation.json'), "r", encoding='utf-8') as f:
+            annotations = json.load(f)
+
+        # process point cloud
+        self.process_point_cloud(scan_id, plydata, annotations)
+
+    def process_scans(self):
+        scan_ids = self.read_all_scans()
+        self.log_starting_info(len(scan_ids))
+
+        if self.num_workers > 1:
+            with parallel_backend('multiprocessing', n_jobs=self.num_workers):
+                Parallel()(delayed(self.scene_proc)(scan_id) for scan_id in tqdm(scan_ids))
+        else:
+            for scan_id in tqdm(scan_ids):
+                self.scene_proc(scan_id)
+
+
+if __name__ == '__main__':
+    cfg = OmegaConf.create({
+        'data_root': './3dod',
+        'save_root': './output',
+        'num_workers': 8,
+        'output': {
+            'pcd': True,
+        }
+    })
+    processor = ARKitScenesProcessor(cfg)
+    processor.process_scans()

data/scene_datasets/ARKitScenes/generate_pcd.py

@@ -0,0 +1,162 @@
+import open3d as o3d
+import matplotlib.pyplot as plt
+import numpy as np
+import os
+import math
+import torch
+from PIL import Image
+import random
+from einops import einsum
+import cv2
+
+@torch.no_grad()
+def get_frustum_mask(points, H, W, intrinsics, view_matrices, near = 0.02, far = 10.):
+
+    ones = torch.ones_like(points[:, 0]).unsqueeze(-1)
+    homo_points = torch.cat([points, ones], dim=-1)
+
+    view_points = einsum(view_matrices, homo_points, "b c, N c -> N b")
+    view_points = view_points[:, :3]
+
+    uv_points = einsum(intrinsics, view_points, "b c, N c -> N b")
+
+    z = uv_points[:, -1:]
+    uv_points = uv_points[:, :2] / z
+    u, v = uv_points[:, 0], uv_points[:, 1]
+    depth = view_points[:, -1]
+    
+    cull_near_fars = (depth >= near) & (depth <= far)
+
+    mask = cull_near_fars & (u >= 0) & (u <= W-1) & (v >= 0) & (v <= H-1)
+    return mask
+
+
+# from ARKitScene code base
+def convert_angle_axis_to_matrix3(angle_axis):
+    """Return a Matrix3 for the angle axis.
+    Arguments:
+        angle_axis {Point3} -- a rotation in angle axis form.
+    """
+    matrix, jacobian = cv2.Rodrigues(angle_axis)
+    return matrix
+
+# from ARKit Scene, some with modifications
+def TrajStringToMatrix(traj_str):
+    """ convert traj_str into translation and rotation matrices
+    Args:
+        traj_str: A space-delimited file where each line represents a camera position at a particular timestamp.
+        The file has seven columns:
+        * Column 1: timestamp
+        * Columns 2-4: rotation (axis-angle representation in radians)
+        * Columns 5-7: translation (usually in meters)
+
+    Returns:
+        Rt: rotation matrix, translation matrix
+    """
+
+    tokens = traj_str.split()
+    assert len(tokens) == 7
+    # Rotation in angle axis
+    angle_axis = [float(tokens[1]), float(tokens[2]), float(tokens[3])]
+    r_w_to_p = convert_angle_axis_to_matrix3(np.asarray(angle_axis))
+    # Translation
+    t_w_to_p = np.asarray([float(tokens[4]), float(tokens[5]), float(tokens[6])])
+    extrinsics = np.eye(4, 4)
+    extrinsics[:3, :3] = r_w_to_p
+    extrinsics[:3, -1] = t_w_to_p
+    Rt = np.linalg.inv(extrinsics)
+    return Rt
+
+
+def st2_camera_intrinsics(filename):
+    w, h, fx, fy, hw, hh = np.loadtxt(filename)
+    return np.asarray([[fx, 0, hw], [0, fy, hh], [0, 0, 1]])
+
+
+def config_parser():
+
+    import configargparse
+    parser = configargparse.ArgumentParser()
+
+
+    # training options
+    parser.add_argument("--near", type=float, default=0., 
+                        help='near distance')
+    parser.add_argument("--far", type=float, default=10., 
+                        help='far distance')
+    parser.add_argument("--camera_height", type=int, default=24, 
+                        help='height of the feature map')
+    parser.add_argument("--camera_width", type=int, default=24, 
+                        help='width of the feature map')
+    parser.add_argument("--feature_fields_search_radius", type=float, default=1., 
+                        help='search radius for near features')
+    parser.add_argument("--feature_fields_search_num", type=int, default=4, 
+                        help='The number of searched near features')
+    parser.add_argument("--mlp_net_layers", type=int, default=8, 
+                        help='layers in mlp network')
+    parser.add_argument("--mlp_net_width", type=int, default=768, 
+                        help='channels per layer in mlp net')
+
+    # rendering options
+    parser.add_argument("--N_samples", type=int, default=512, 
+                        help='number of coarse samples per ray')
+    parser.add_argument("--N_importance", type=int, default=16,
+                        help='number of fine samples per ray')
+
+    return parser
+
+
+parser = config_parser()
+args, unknown = parser.parse_known_args() #parser.parse_args()  
+
+
+scene_list = os.listdir('3dod/Training')
+image_list = []
+for scene_id in scene_list:
+    
+    image_path = '3dod/Training/'+scene_id+'/'+scene_id+'_frames/lowres_wide'
+    image_list = os.listdir(image_path)
+    image_list.sort()
+    extrinsic_file = '3dod/Training/'+scene_id+'/'+scene_id+'_frames/lowres_wide.traj'
+    with open(extrinsic_file, 'r') as file:  
+        extrinsic_list = [line.strip() for line in file]
+
+    image_ids = [i for i in range(len(image_list))]
+    random.shuffle(image_ids)
+    image_ids = image_ids[:30]
+
+    image_list = [image_path+'/'+image_list[i] for i in image_ids]
+    extrinsic_list = [extrinsic_list[i] for i in image_ids]
+
+    #image_list = random.sample(image_list,min(30,len(image_list)))
+    pcd_all = o3d.geometry.PointCloud()
+    extrinsic_id = 0
+    for image_path in image_list:
+        intrinsic_file = '3dod/Training/'+scene_id+'/'+scene_id+'_frames'+'/lowres_wide_intrinsics/' + image_path.split('/')[-1][:-4]+'.pincam'
+        with open(intrinsic_file, 'r') as file:  
+            intrinsic_raw = [line.split() for line in file]
+        intrinsic = st2_camera_intrinsics(intrinsic_raw[0])
+        
+        extrinsic = TrajStringToMatrix(extrinsic_list[extrinsic_id])
+        extrinsic_id += 1
+
+        R = extrinsic[:3,:3]
+        T = extrinsic[:3,3:4]
+        color_raw = o3d.io.read_image(image_path)
+        depth_raw = o3d.io.read_image(image_path.replace("lowres_wide","lowres_depth"))
+        #rgbd_image = o3d.geometry.RGBDImage.create_from_color_and_depth(color_raw, depth_raw, depth_scale=1000.0, depth_trunc=1000.0, convert_rgb_to_intensity=False)
+
+        #pcd = o3d.geometry.PointCloud.create_from_rgbd_image(rgbd_image,o3d.camera.PinholeCameraIntrinsic(256,192,intrinsic[0][0],intrinsic[1][1],intrinsic[0][2],intrinsic[1][2]))
+        pcd = o3d.geometry.PointCloud.create_from_depth_image(depth_raw, o3d.camera.PinholeCameraIntrinsic(256,192,intrinsic[0][0],intrinsic[1][1],intrinsic[0][2],intrinsic[1][2]), depth_scale=1000.0, depth_trunc=1000.0)
+
+        points = np.asarray(pcd.points)
+        
+        points = (R @ points.T + T).T
+        pcd.points = o3d.utility.Vector3dVector(points)
+        pcd_all += pcd
+        #o3d.visualization.draw_geometries([pcd_all])
+        #mask = get_frustum_mask(torch.tensor(np.array(pcd_all.points)), 240, 320, intrinsic[:3,:3], np.linalg.inv(extrinsic), near = 0.02, far = 10.)
+        
+    
+    
+    o3d.visualization.draw_geometries([pcd_all])

data/scene_datasets/ScanNet/generate_pcd.py

@@ -0,0 +1,155 @@
+import open3d as o3d
+import matplotlib.pyplot as plt
+import numpy as np
+import os
+import math
+import torch
+from PIL import Image
+import random
+from einops import einsum
+
+
+@torch.no_grad()
+def get_frustum_mask(points, H, W, intrinsics, view_matrices, near = 0.02, far = 10.):
+
+    ones = torch.ones_like(points[:, 0]).unsqueeze(-1)
+    homo_points = torch.cat([points, ones], dim=-1)
+
+    view_points = einsum(view_matrices, homo_points, "b c, N c -> N b")
+    view_points = view_points[:, :3]
+
+    uv_points = einsum(intrinsics, view_points, "b c, N c -> N b")
+
+    z = uv_points[:, -1:]
+    uv_points = uv_points[:, :2] / z
+    u, v = uv_points[:, 0], uv_points[:, 1]
+    depth = view_points[:, -1]
+    
+    cull_near_fars = (depth >= near) & (depth <= far)
+
+    mask = cull_near_fars & (u >= 0) & (u <= W-1) & (v >= 0) & (v <= H-1)
+    return mask
+
+
+def config_parser():
+
+    import configargparse
+    parser = configargparse.ArgumentParser()
+
+
+    # training options
+    parser.add_argument("--near", type=float, default=0., 
+                        help='near distance')
+    parser.add_argument("--far", type=float, default=10., 
+                        help='far distance')
+    parser.add_argument("--camera_height", type=int, default=24, 
+                        help='height of the feature map')
+    parser.add_argument("--camera_width", type=int, default=24, 
+                        help='width of the feature map')
+    parser.add_argument("--feature_fields_search_radius", type=float, default=1., 
+                        help='search radius for near features')
+    parser.add_argument("--feature_fields_search_num", type=int, default=4, 
+                        help='The number of searched near features')
+    parser.add_argument("--mlp_net_layers", type=int, default=8, 
+                        help='layers in mlp network')
+    parser.add_argument("--mlp_net_width", type=int, default=768, 
+                        help='channels per layer in mlp net')
+
+    # rendering options
+    parser.add_argument("--N_samples", type=int, default=512, 
+                        help='number of coarse samples per ray')
+    parser.add_argument("--N_importance", type=int, default=16,
+                        help='number of fine samples per ray')
+
+    return parser
+
+
+parser = config_parser()
+args, unknown = parser.parse_known_args() #parser.parse_args()  
+
+camera_intrinsic = np.eye(4)
+with open('scannet_train_images/frames_square/scene0000_00/intrinsic_depth.txt', 'r') as file:  
+    numbers = [line.strip() for line in file]
+for i in range(4):  
+    for j in range(4): 
+        camera_intrinsic[i][j] = float(numbers[i].split()[j])
+camera_intrinsic[0][0] *= args.camera_width / 320
+camera_intrinsic[1][1] *= args.camera_height / 240
+
+N_spacing = (args.far - args.near) / args.N_samples
+sampled_points = o3d.geometry.PointCloud()
+for N_index in range(args.N_samples):
+    N_distance = args.near + N_spacing * (N_index+1)
+    N_depth = np.full((args.camera_height,args.camera_width),N_distance,dtype=np.float32)
+    N_depth = o3d.geometry.Image(N_depth)
+    N_points = o3d.geometry.PointCloud.create_from_depth_image(N_depth, o3d.camera.PinholeCameraIntrinsic(args.camera_width,args.camera_height,camera_intrinsic[0][0]/2.,camera_intrinsic[1][1]/2.,args.camera_width/2,args.camera_height/2), depth_scale=1., depth_trunc=1.)
+    sampled_points += N_points
+points_along_rays = o3d.geometry.PointCloud()
+points_along_rays += sampled_points
+
+scene_list = []
+for i in range(800):
+    path = 'scannet_train_images/frames_square/'
+    scene = 'scene'+str(i).rjust(4, "0")+'_00/'    
+    scene_list.append(path+scene)
+
+for scene_id in scene_list:
+    image_list = []
+    for image_id in range(1000):
+        image_id = image_id * 20   
+        image_path = scene_id + 'color/' + str(image_id) + ".jpg"
+        if not os.path.exists(image_path):
+            break
+        image_list.append(str(image_id))
+
+    image_list = image_list[:30]
+    #image_list = random.sample(image_list,min(30,len(image_list)))
+    target_image = random.choice(image_list)
+    pcd_all = o3d.geometry.PointCloud() 
+    for image_id in image_list:
+        intrinsic = np.eye(4)
+        with open(scene_id + 'intrinsic_depth.txt', 'r') as file:  
+            intrinsic_raw = [line.strip() for line in file]
+        for i in range(4):  
+            for j in range(4): 
+                intrinsic[i][j] = float(intrinsic_raw[i].split()[j])
+
+        extrinsic = np.eye(4)
+        with open(scene_id + 'pose/' + image_id + '.txt', 'r') as file:  
+            extrinsic_raw = [line.strip() for line in file]
+ 
+        for i in range(4):  
+            for j in range(4): 
+                extrinsic[i][j] = float(extrinsic_raw[i].split()[j])
+
+        R = extrinsic[:3,:3]
+        T = extrinsic[:3,3:4]
+        if image_id == target_image:
+            points = np.asarray(sampled_points.points)
+            points = (R @ points.T + T).T
+            points_along_rays.points = o3d.utility.Vector3dVector(points)
+            continue
+
+        color_raw = o3d.io.read_image(scene_id + 'color/' + image_id + ".jpg")
+        depth_raw = o3d.io.read_image(scene_id + 'depth/' + image_id + ".png")
+        rgbd_image = o3d.geometry.RGBDImage.create_from_color_and_depth(color_raw, depth_raw, depth_scale=1000.0, depth_trunc=1000.0, convert_rgb_to_intensity=False)   
+
+        # modify the intrinsic, because the image resolution is changed
+        intrinsic[0][0],intrinsic[1][1],intrinsic[0][2],intrinsic[1][2] = intrinsic[0][0]/2,intrinsic[1][1]/2,intrinsic[0][2]/2,intrinsic[1][2]/2
+
+        pcd = o3d.geometry.PointCloud.create_from_rgbd_image(rgbd_image,o3d.camera.PinholeCameraIntrinsic(320,240,intrinsic[0][0],intrinsic[1][1],intrinsic[0][2],intrinsic[1][2]))
+        #pcd = o3d.geometry.PointCloud.create_from_depth_image(depth_raw, o3d.camera.PinholeCameraIntrinsic(320,240,intrinsic[0][0],intrinsic[1][1],intrinsic[0][2],intrinsic[1][2]), depth_scale=1000.0, depth_trunc=1000.0)
+
+        points = np.asarray(pcd.points)
+        
+        points = (R @ points.T + T).T
+        pcd.points = o3d.utility.Vector3dVector(points)
+        pcd_all += pcd
+        #o3d.visualization.draw_geometries([pcd_all])
+        mask = get_frustum_mask(torch.tensor(np.array(pcd_all.points)), 240, 320, intrinsic[:3,:3], np.linalg.inv(extrinsic), near = 0.02, far = 10.)
+        
+        #exit()
+    #pcd_all += points_along_rays
+    
+    
+    o3d.visualization.draw_geometries([pcd_all])

data/scene_datasets/ScanNet/get_scannet_align_matrix.py

@@ -0,0 +1,84 @@
+import os
+import argparse
+import json
+import numpy as np
+import pprint
+import time
+import multiprocessing as mp
+from functools import partial
+
+from plyfile import PlyData
+from tqdm import tqdm
+import torch
+
+dict_align_matrix ={}
+
+def process_per_scan(scan_id, scan_dir):
+
+    global dict_align_matrix
+  
+    # Load point clouds with colors
+    with open(os.path.join(scan_dir, scan_id, '%s_vh_clean_2.ply'%(scan_id)), 'rb') as f:
+        plydata = PlyData.read(f) # elements: vertex, face
+    points = np.array([list(x) for x in plydata.elements[0]]) # [[x, y, z, r, g, b, alpha]]
+    coords = np.ascontiguousarray(points[:, :3])
+    colors = np.ascontiguousarray(points[:, 3:6])
+    # # TODO: normalize the coords and colors
+    # coords = coords - coords.mean(0)
+    # colors = colors / 127.5 - 1
+
+   
+    align_matrix = np.eye(4)
+    with open(os.path.join(scan_dir, scan_id, '%s.txt'%(scan_id)), 'r') as f:
+        for line in f:
+            if line.startswith('axisAlignment'):
+                align_matrix = np.array([float(x) for x in line.strip().split()[-16:]]).astype(np.float32).reshape(4, 4)
+                break
+    # Transform the points
+    pts = np.ones((coords.shape[0], 4), dtype=coords.dtype)
+    pts[:, 0:3] = coords
+    coords = np.dot(pts, align_matrix.transpose())[:, :3]  # Nx4
+    dict_align_matrix[scan_id] = align_matrix.tolist()
+    # Make sure no nans are introduced after conversion
+    assert (np.sum(np.isnan(coords)) == 0)
+    
+
+def parse_args():
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument('--scannet_dir', required=True, type=str,
+                        help='the path to the downloaded ScanNet scans')
+
+    # Optional arguments.
+    parser.add_argument('--num_workers', default=-1, type=int,
+                        help='the number of processes, -1 means use the available max')
+    parser.add_argument('--apply_global_alignment', default=True, action='store_true',
+                        help='rotate/translate entire scan globally to aligned it with other scans')
+    args = parser.parse_args()
+
+    # Print the args
+    args_string = pprint.pformat(vars(args))
+    print(args_string)
+
+    return args
+
+
+def main():
+    args = parse_args()
+    
+    # for split in ['scans', 'scans_test']:
+    for split in ['scans']:
+        scannet_dir = os.path.join(args.scannet_dir, split)
+
+        scan_ids = os.listdir(scannet_dir)
+        scan_ids.sort()
+        print(split, '%d scans' % (len(scan_ids)))
+
+        for scan_id in tqdm(scan_ids):
+            process_per_scan(scan_id=scan_id,scan_dir=scannet_dir)
+
+    global dict_align_matrix
+    json.dump(dict_align_matrix,open("scannet_align_matrix.json","w"))
+
+if __name__ == '__main__':
+    main()

data/scene_datasets/ScanNet/scannetv2_test.txt

@@ -0,0 +1,100 @@
+scene0707_00
+scene0708_00
+scene0709_00
+scene0710_00
+scene0711_00
+scene0712_00
+scene0713_00
+scene0714_00
+scene0715_00
+scene0716_00
+scene0717_00
+scene0718_00
+scene0719_00
+scene0720_00
+scene0721_00
+scene0722_00
+scene0723_00
+scene0724_00
+scene0725_00
+scene0726_00
+scene0727_00
+scene0728_00
+scene0729_00
+scene0730_00
+scene0731_00
+scene0732_00
+scene0733_00
+scene0734_00
+scene0735_00
+scene0736_00
+scene0737_00
+scene0738_00
+scene0739_00
+scene0740_00
+scene0741_00
+scene0742_00
+scene0743_00
+scene0744_00
+scene0745_00
+scene0746_00
+scene0747_00
+scene0748_00
+scene0749_00
+scene0750_00
+scene0751_00
+scene0752_00
+scene0753_00
+scene0754_00
+scene0755_00
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