Duplicate from the-masses/ReplicaOcc

Co-authored-by: JIANG ZEYU <the-masses@users.noreply.huggingface.co>

.gitattributes

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+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
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LICENSE

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+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   Copyright 2026 Zeyu Jiang, Changqing Zhou, Xingxing Zuo, Changhao Chen
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.

README.md

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+---
+license: apache-2.0
+pretty_name: ReplicaOcc
+size_categories:
+- 10K<n<100K
+tags:
+- 3d
+- robotics
+- slam
+- rgb-d
+- occupancy
+- embodied-ai
+- replica
+---
+
+# Replica_OCC Benchmark
+
+Replica_OCC is a Replica-based occupancy benchmark constructed in the data organization style of [EmbodiedOcc-ScanNet](https://huggingface.co/datasets/YkiWu/EmbodiedOcc-ScanNet) and [OccScanNet](https://huggingface.co/datasets/hongxiaoy/OccScanNet). It provides RGB-D sequences and scene-level occupancy ground truth for evaluating embodied occupancy prediction systems.
+
+Ground-truth occupancy and poses are intended for evaluation-time alignment and metric computation. They are not required for training FreeOcc and are not used for map construction.
+
+## Citation
+
+If you use Replica_OCC with [FreeOcc](https://the-masses.github.io/freeocc-web/), please cite:
+
+```bibtex
+@article{jiang2026freeocc,
+  title={FreeOcc: Training-Free Embodied Open-Vocabulary Occupancy Prediction},
+  author={Jiang, Zeyu and Zhou, Changqing and Zuo, Xingxing and Chen, Changhao},
+  journal={arXiv preprint arXiv:2604.28115},
+  year={2026}
+}
+```
+
+## Released Files
+
+The released dataset package contains:
+
+```text
+Replica_OCC/
+├── README.md
+├── replica_name.txt
+├── prepare_preprocessed.py
+├── prepare_scene_occ.py
+├── vis_preprocessed.py
+├── vis_scene_occ.py
+└── Replica_OCC/
+    ├── preprocessed/
+    ├── global_occ_package/
+    └── sequences/
+```
+
+Only the four preparation/visualization scripts above are part of the released benchmark utilities.
+
+## Dataset Layout
+
+```text
+Replica_OCC/
+├── preprocessed/
+│   ├── office0.npy
+│   ├── office1.npy
+│   ├── office2.npy
+│   ├── office3.npy
+│   ├── office4.npy
+│   ├── room0.npy
+│   ├── room1.npy
+│   └── room2.npy
+├── global_occ_package/
+│   ├── office0.pkl
+│   ├── office1.pkl
+│   ├── office2.pkl
+│   ├── office3.pkl
+│   ├── office4.pkl
+│   ├── room0.pkl
+│   ├── room1.pkl
+│   └── room2.pkl
+└── sequences/
+    ├── cam_params.json
+    ├── office0/
+    │   ├── color/
+    │   │   ├── 0.jpg
+    │   │   └── ...
+    │   ├── depth/
+    │   │   ├── 0.png
+    │   │   └── ...
+    │   ├── pose/
+    │   │   ├── 0.txt
+    │   │   └── ...
+    │   └── intrinsic/
+    │       ├── intrinsic_color.txt
+    │       ├── intrinsic_depth.txt
+    │       ├── extrinsic_color.txt
+    │       └── extrinsic_depth.txt
+    ├── office1/
+    ├── office2/
+    ├── office3/
+    ├── office4/
+    ├── room0/
+    ├── room1/
+    └── room2/
+```
+
+For FreeOcc evaluation, use:
+
+```bash
+DATA_ROOT=/path/to/Replica_OCC/sequences
+SCENE_OCC_ROOT=/path/to/Replica_OCC
+```
+
+## Coordinate System
+
+The occupancy ground truth is built in the original Replica world coordinate system.
+
+`prepare_preprocessed.py` back-projects depth pixels using Replica camera poses and intrinsics. The resulting sparse semantic voxel points are stored in Replica world coordinates. `prepare_scene_occ.py` then builds a dense occupancy grid from those points and saves voxel center coordinates in the same Replica world coordinate system.
+
+## Preparation Pipeline
+
+The benchmark is created in two main stages, followed by optional visualization checks.
+
+### 1. Build Sparse Semantic Voxels
+
+`prepare_preprocessed.py` reads raw Replica RGB-D/semantic data and camera poses. For each scene, it back-projects depth pixels into 3D world points, assigns semantic labels from the semantic-id images, and voxelizes them by majority vote.
+
+Input expected by the script:
+
+```text
+Replica_SLAM/
+├── cam_params.json
+├── office0/
+│   ├── depths/depth000000.png
+│   ├── semantic_ids/semantic_id000000.png
+│   └── traj.txt
+└── ...
+```
+
+Example command:
+
+```bash
+python prepare_preprocessed.py \
+  --replica_root ./Replica_SLAM \
+  --out_root ./Replica_OCC \
+  --stride 4 \
+  --depth_scale -1 \
+  --max_depth 10.0 \
+  --max_frames -1
+```
+
+Output:
+
+```text
+Replica_OCC/preprocessed/<scene>.npy
+```
+
+Each `.npy` stores an array of shape `(N, 7)`:
+
+```text
+x, y, z, r, g, b, label
+```
+
+The RGB columns are placeholders; the semantic label is stored in the last column.
+
+### 2. Inspect Sparse Voxels
+
+`vis_preprocessed.py` visualizes the sparse semantic voxels produced by the previous step.
+
+```bash
+python vis_preprocessed.py \
+  --npy ./Replica_OCC/preprocessed/office0.npy
+```
+
+This is mainly a sanity check for depth back-projection and semantic labels.
+
+### 3. Build Scene-Level Occupancy Packages
+
+`prepare_scene_occ.py` converts `preprocessed/<scene>.npy` into a dense scene-level occupancy package. It builds a regular voxel grid, assigns labels by nearest-neighbor lookup, and computes an observed-space mask by projecting voxels into Replica depth frames.
+
+Example command:
+
+```bash
+python prepare_scene_occ.py \
+  --replica_root ./Replica_SLAM \
+  --preprocessed_dir ./Replica_OCC/preprocessed \
+  --out_dir ./Replica_OCC/global_occ_package \
+  --obs_stride_frame 1 \
+  --obs_stride_pix 1 \
+  --mask_dilate 0 \
+  --obs_max_frames -1 \
+  --max_depth 10.0
+```
+
+Output:
+
+```text
+Replica_OCC/global_occ_package/<scene>.pkl
+```
+
+Each `.pkl` contains:
+
+```text
+scene_name       scene id
+scene_dim        dense occupancy grid dimensions
+global_pts       dense voxel centers in Replica world coordinates
+global_labels    voxel labels
+global_mask      observed-space mask
+valid_img_count  number of depth images used for mask construction
+valid_img_paths  image paths used by the mask builder
+```
+
+Label convention:
+
+```text
+0    known free space
+>0   occupied semantic label
+255  unknown / unobserved
+```
+
+### 4. Inspect Final Occupancy Packages
+
+`vis_scene_occ.py` visualizes the final scene-level occupancy package.
+
+```bash
+python vis_scene_occ.py \
+  --pkl ./Replica_OCC/global_occ_package/office0.pkl \
+  --downsample 1
+```
+
+Visualization color meaning:
+
+```text
+mask = 0              unknown region
+mask = 1, label = 0   known free space
+mask = 1, label > 0   occupied semantic voxels
+```
+
+## Labels
+
+`replica_name.txt` stores the semantic class names used by Replica_OCC. Evaluation and visualization scripts can use this file to map semantic label ids to readable names.
+
+## Scenes
+
+Replica_OCC contains the following eight Replica scenes:
+
+```text
+office0
+office1
+office2
+office3
+office4
+room0
+room1
+room2
+```
+
+## Notes
+
+- `preprocessed/*.npy` is an intermediate representation used to reproduce `global_occ_package/*.pkl`.
+- `global_occ_package/*.pkl` is the occupancy ground truth used by evaluation.
+- `sequences/*` is the RGB-D input used by SLAM and Gaussian mapping.
+- The dataset follows a ScanNet-like RGB-D folder layout so it can be used by FreeOcc's shared dataloader.

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Replica_OCC/sequences/cam_params.json

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+        "w": 1200,
+        "h": 680,
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+        "fy": 600.0,
+        "cx": 599.5,
+        "cy": 339.5,
+        "scale": 6553.5
+    }
+}