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.gitignore

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+# Ignore raw scene folders
+Real/
+Hypothetical/
+Multi_View/

.vscode/settings.json

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+{
+    "python.analysis.autoImportCompletions": true
+}

Causal3D.py

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+import datasets
+import pandas as pd
+import os
+from pathlib import Path
+from tqdm import tqdm
+
+_CITATION = """\
+@article{liu2025causal3d,
+  title={CAUSAL3D: A Comprehensive Benchmark for Causal Learning from Visual Data},
+  author={Liu, Disheng and Qiao, Yiran and Liu, Wuche and Lu, Yiren and Zhou, Yunlai and Liang, Tuo and Yin, Yu and Ma, Jing},
+  journal={arXiv preprint arXiv:2503.04852},
+  year={2025}
+}
+"""
+
+_DESCRIPTION = """\
+Causal3D is a benchmark for evaluating causal reasoning in physical and hypothetical visual scenes. 
+It includes both real-world recordings and rendered synthetic scenes demonstrating causal interactions.
+"""
+
+_HOMEPAGE = "https://huggingface.co/datasets/LLDDSS/Causal3D"
+_LICENSE = "CC-BY-4.0"
+
+class Causal3D(datasets.GeneratorBasedBuilder):
+    DEFAULT_CONFIG_NAME = "Real_Water_flow"
+    BUILDER_CONFIGS = [
+        # hypothetical_scenes
+        datasets.BuilderConfig(name="Hypothetical_V2_linear", version=datasets.Version("1.0.0"), description="Hypothetical_V2_linear scene"),
+        datasets.BuilderConfig(name="Hypothetical_V2_nonlinear", version=datasets.Version("1.0.0"), description="Hypothetical_V2_nonlinear scene"),
+        datasets.BuilderConfig(name="Hypothetical_V3_fully_connected_linear", version=datasets.Version("1.0.0"), description="Hypothetical_V3_fully_connected_linear scene"),
+        datasets.BuilderConfig(name="Hypothetical_V3_v_structure_linear", version=datasets.Version("1.0.0"), description="Hypothetical_V3_v_structure_linear scene"),
+        datasets.BuilderConfig(name="Hypothetical_V3_v_structure_nonlinear", version=datasets.Version("1.0.0"), description="Hypothetical_V3_v_structure_nonlinear scene"),
+        datasets.BuilderConfig(name="Hypothetical_V4_linear", version=datasets.Version("1.0.0"), description="Hypothetical_V4_linear scene"),
+        datasets.BuilderConfig(name="Hypothetical_V4_v_structure_nonlinear", version=datasets.Version("1.0.0"), description="Hypothetical_V4_v_structure_nonlinear scene"),
+        datasets.BuilderConfig(name="Hypothetical_V4_v_structure_linear", version=datasets.Version("1.0.0"), description="Hypothetical_V4_v_structure_linear scene"),
+        datasets.BuilderConfig(name="Hypothetical_V5_linear", version=datasets.Version("1.0.0"), description="Hypothetical_V5_linear scene"),
+        datasets.BuilderConfig(name="Hypothetical_V5_v_structure_linear", version=datasets.Version("1.0.0"), description="Hypothetical_V5_v_structure_linear scene"),
+        datasets.BuilderConfig(name="Hypothetical_V5_v_structure_nonlinear", version=datasets.Version("1.0.0"), description="Hypothetical_V5_v_structure_nonlinear scene"),
+
+        # real_scenes
+        datasets.BuilderConfig(name="Real_Parabola", version=datasets.Version("1.0.0"), description="Real_Parabola scene"),
+        datasets.BuilderConfig(name="Real_Magnet", version=datasets.Version("1.0.0"), description="Real_Magnet scene"),
+        datasets.BuilderConfig(name="Real_Spring", version=datasets.Version("1.0.0"), description="Real_Spring scene"),
+        datasets.BuilderConfig(name="Real_Water_flow", version=datasets.Version("1.0.0"), description="Real_Water_flow scene"),
+        datasets.BuilderConfig(name="Real_Seesaw", version=datasets.Version("1.0.0"), description="Real_Seesaw scene"),
+        datasets.BuilderConfig(name="Real_Reflection", version=datasets.Version("1.0.0"), description="Real_Reflection scene"),
+        datasets.BuilderConfig(name="Real_Pendulum", version=datasets.Version("1.0.0"), description="Real_Pendulum scene"),
+        datasets.BuilderConfig(name="Real_Convex_len", version=datasets.Version("1.0.0"), description="Real_Convex_len scene"),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features({
+                "image": datasets.Image(),
+                "file_name": datasets.Value("string"),
+                "metadata": datasets.Value("string"),  # optionally replace with structured fields
+            }),
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+    
+    def _split_generators(self, dl_manager):
+        parts = self.config.name.split("_", 2)
+        category = parts[0] + "_" + parts[1]
+        if category not in ["real_scenes", "hypothetical_scenes"]:
+            raise ValueError(f"Invalid category '{category}'.")
+
+        scene = parts[2]
+        scene_path = os.path.join(category, scene)
+
+        if os.path.exists(scene_path):  
+          data_dir = scene_path
+        else: 
+          archive_path = dl_manager.download_and_extract(f"{scene_path}.zip")
+          data_dir = os.path.join(archive_path, scene)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"data_dir": data_dir},
+            )
+        ]
+    def _generate_examples(self, data_dir):
+        def color(text, code):
+            return f"\033[{code}m{text}\033[0m"
+        print("load data from {}".format(data_dir))  
+        try:
+            image_files = {}
+            for ext in ("*.png", "*.jpg", "*.jpeg"):
+                for img_path in Path(data_dir).rglob(ext):
+                    relative_path = str(img_path.relative_to(data_dir))
+                    image_files[relative_path] = str(img_path)
+            parts = [i.split('/')[0] for i in list(image_files.keys())]
+            parts = set(parts)
+            if "part_000" not in parts:
+               parts= ['']
+
+
+        except Exception as e:
+            print(color(f"Error loading images: {e}", "31"))  # Red
+            return
+    
+        # Find the .csv file
+        csv_files = list(Path(data_dir).rglob("*.csv"))
+        csv_files = [f for f in Path(data_dir).rglob("*.csv") if not f.name.startswith("._")]
+        if not csv_files:
+            # print(f"\033[33m[SKIP] No CSV found in {data_dir}, skipping this config.\033[0m")
+            pass
+        # print(f"\033[33m[INFO] Found CSV: {csv_files}\033[0m")
+        csv_path = csv_files[0] if csv_files else None
+        df = pd.read_csv(csv_path) if csv_path else None
+        image_col_exists = True
+        if df is not None and "image" not in df.columns:
+            image_col_exists = False
+                
+        images = df["image"].tolist() if image_col_exists and df is not None else []
+        images = [i.split('/')[-1].split('.')[0] for i in images if i.endswith(('.png', '.jpg', '.jpeg'))]
+
+        try:
+            # Match CSV rows with image paths
+            if df is None:
+                for i, j in tqdm(image_files.items(), desc="Processing images", unit="image"):
+                    yield i, {
+                        "image": j,
+                        "file_name": i,
+                        "metadata": None,
+                    }
+
+            else:
+                for idx, row in tqdm(df.iterrows(), total=len(df), desc="Processing rows", unit="row"):
+                    fname = row["ID"]
+                    raw_record_img_path = images[idx] if images else "" #row["image"] 
+                    record_img_name = raw_record_img_path.split('/')[-1]
+                    for part in parts:
+                        if part == '':
+                            record_img_path = record_img_name
+                        else:
+                            record_img_path = "/".join([part, record_img_name.strip()])
+                            if "Water_flow_scene_render" in data_dir:
+                                record_img_path = "/".join([part, str(int(record_img_name.strip().split('.')[0]))+".png"])
+                        if record_img_path in image_files:
+                            # print(color(f"record_img_path: { image_files[record_img_path]}", "34"))  # Blue
+                            yield idx, {
+                                "image": image_files[record_img_path],
+                                "file_name": fname,
+                                "metadata": row.to_json(),
+                            }
+                            break
+                            
+                        else:
+                            yield idx, {
+                                # "image": "",
+                                "file_name": fname,
+                                "metadata": row.to_json(),
+                            }
+                            break
+
+
+        except Exception as e:
+            print(color(f"Error processing CSV rows: {e}", "31"))
+        

Code/explore.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "cc0b451f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import pandas as pd\n",
+    "\n",
+    "base = os.getcwd()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e3b39b13",
+   "metadata": {},
+   "source": [
+    "# Real"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "85e4d8c2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'base: /Users/dsl/Desktop/Causal3D_Dataset/Code'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'data_path: /Users/dsl/Desktop/Causal3D_Dataset/Code/../Real'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\"Scene: Convex_len, Number of files: 10001, File types: {'.csv', '.png'}\""
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\"Scene: Seesaw, Number of files: 10001, File types: {'.csv', '.png'}\""
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\"Scene: Pendulum, Number of files: 10001, File types: {'.csv', '.png'}\""
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\"Scene: Water_flow, Number of files: 10001, File types: {'.csv', '.png'}\""
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\"Scene: Parabola, Number of files: 10001, File types: {'.csv', '.png'}\""
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\"Scene: Magnet, Number of files: 10001, File types: {'.csv', '.png'}\""
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\"Scene: Spring, Number of files: 10001, File types: {'.csv', '.png'}\""
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\"Scene: Reflection, Number of files: 10001, File types: {'.csv', '.png'}\""
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import display\n",
+    "\n",
+    "base = os.getcwd()\n",
+    "data_path = os.path.join(base, \"../Real\")\n",
+    "scenes = os.listdir(data_path)\n",
+    "\n",
+    "display(f\"base: {base}\")\n",
+    "display(f\"data_path: {data_path}\")\n",
+    "all_scenes = [scene for scene in scenes if os.path.isdir(os.path.join(data_path, scene))]\n",
+    "# get the number of files and the corresponding type under the each scene\n",
+    "for scene in all_scenes:\n",
+    "    scene_path = os.path.join(data_path, scene)\n",
+    "    files = os.listdir(scene_path)\n",
+    "    file_types = set([os.path.splitext(file)[1] for file in files])\n",
+    "    display(f\"Scene: {scene}, Number of files: {len(files)}, File types: {file_types}\") \n",
+    "    # You see an empty string '' in file_types because some files in the directory do not have an extension.\n",
+    "    # For example, hidden files like '.DS_Store' or files without a dot will result in an empty extension from os.path.splitext.\n",
+    "    # To check which files have no extension:\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "6979c835",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'Convex_len': ['/Users/dsl/Desktop/Causal3D_Dataset/Code/../Real/Convex_len/tabular.csv'],\n",
+       " 'Seesaw': ['/Users/dsl/Desktop/Causal3D_Dataset/Code/../Real/Seesaw/tabular.csv'],\n",
+       " 'Pendulum': ['/Users/dsl/Desktop/Causal3D_Dataset/Code/../Real/Pendulum/tabular.csv'],\n",
+       " 'Water_flow': ['/Users/dsl/Desktop/Causal3D_Dataset/Code/../Real/Water_flow/tabular.csv'],\n",
+       " 'Parabola': ['/Users/dsl/Desktop/Causal3D_Dataset/Code/../Real/Parabola/tabular.csv'],\n",
+       " 'Magnet': ['/Users/dsl/Desktop/Causal3D_Dataset/Code/../Real/Magnet/tabular.csv'],\n",
+       " 'Spring': ['/Users/dsl/Desktop/Causal3D_Dataset/Code/../Real/Spring/tabular.csv'],\n",
+       " 'Reflection': ['/Users/dsl/Desktop/Causal3D_Dataset/Code/../Real/Reflection/tabular.csv']}"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scene_csv_paths = {}\n",
+    "for scene in all_scenes:\n",
+    "    folder = os.path.join(data_path, scene)\n",
+    "    if not os.path.isdir(folder):\n",
+    "        continue\n",
+    "    csv_files = [f for f in os.listdir(folder) if f.endswith('.csv')]\n",
+    "    scene_csv_paths[scene] = [os.path.join(folder, f) for f in csv_files]\n",
+    "scene_csv_paths\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "c58bbbd3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'Convex_len': ['/Users/dsl/Desktop/Causal3D_Dataset/Code/../Real/Convex_len/tabular.csv'],\n",
+       " 'Seesaw': ['/Users/dsl/Desktop/Causal3D_Dataset/Code/../Real/Seesaw/tabular.csv'],\n",
+       " 'Pendulum': ['/Users/dsl/Desktop/Causal3D_Dataset/Code/../Real/Pendulum/tabular.csv'],\n",
+       " 'Water_flow': ['/Users/dsl/Desktop/Causal3D_Dataset/Code/../Real/Water_flow/tabular.csv'],\n",
+       " 'Parabola': ['/Users/dsl/Desktop/Causal3D_Dataset/Code/../Real/Parabola/tabular.csv'],\n",
+       " 'Magnet': ['/Users/dsl/Desktop/Causal3D_Dataset/Code/../Real/Magnet/tabular.csv'],\n",
+       " 'Spring': ['/Users/dsl/Desktop/Causal3D_Dataset/Code/../Real/Spring/tabular.csv'],\n",
+       " 'Reflection': ['/Users/dsl/Desktop/Causal3D_Dataset/Code/../Real/Reflection/tabular.csv']}"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scene_csv_paths"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "54888282",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "# === Function to convert filenames ===\n",
+    "def convert_filename(filename):\n",
+    "    if isinstance(filename, str) and filename.endswith('.png'):\n",
+    "        # Remove leading zeros from the number part\n",
+    "        number_part = filename.split('.')[0]\n",
+    "        new_number = str(int(number_part))\n",
+    "        return f\"{new_number}.png\"\n",
+    "    return filename  # if not a string or doesn't match, return as is\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "9b3afa52",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# check each csv file, are there all of png files exists\n",
+    "for i in scene_csv_paths:\n",
+    "    df = pd.read_csv(scene_csv_paths[i][0])\n",
+    "    df['imgs'] = df['imgs'].apply(convert_filename)\n",
+    "    df.to_csv(scene_csv_paths[i][0], index=False)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "50723819",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# check each csv file, are there all of png files exists\n",
+    "for i in scene_csv_paths:\n",
+    "    df = pd.read_csv(scene_csv_paths[i][0])\n",
+    "    imgs = df['imgs'].tolist()\n",
+    "    for img in imgs:\n",
+    "        img_path = os.path.join(data_path, i, img)\n",
+    "        if not os.path.exists(img_path):\n",
+    "            print(f\"Image {img} does not exist in scene {i}.\")\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8d7f2bad",
+   "metadata": {},
+   "source": [
+    "# Hypothetic\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "d9c4b659",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Scene: V4_linear, Number of files: 10003, File types: {'.png': 10001, '': 1, '.csv': 1}\n",
+      "Scene: V4_v_structure_linear, Number of files: 10001, File types: {'.png': 10000, '.csv': 1}\n",
+      "Scene: V3_fully_connected_linear, Number of files: 10002, File types: {'.png': 10000, '.md': 1, '.csv': 1}\n",
+      "Scene: V2_linear, Number of files: 10002, File types: {'.png': 10000, '.md': 1, '.csv': 1}\n",
+      "Scene: V2_nonlinear, Number of files: 10002, File types: {'.png': 10000, '.md': 1, '.csv': 1}\n",
+      "Scene: V5_linear, Number of files: 10001, File types: {'.png': 10000, '.csv': 1}\n",
+      "Scene: V3_v_structure_nonlinear, Number of files: 10002, File types: {'.png': 10000, '': 1, '.csv': 1}\n",
+      "Scene: V3_v_structure_linear, Number of files: 10002, File types: {'.png': 10000, '': 1, '.csv': 1}\n",
+      "Scene: V5_v_structure_nonlinear, Number of files: 10001, File types: {'.png': 10000, '.csv': 1}\n",
+      "Scene: V5_v_structure_linear, Number of files: 10001, File types: {'.png': 10000, '.csv': 1}\n",
+      "Scene: V4_v_strcuture_nonlinear, Number of files: 10001, File types: {'.png': 10000, '.csv': 1}\n"
+     ]
+    }
+   ],
+   "source": [
+    "hy_base_path = os.path.join(base, \"../Hypothetic\")\n",
+    "hy_scenes = os.listdir(hy_base_path)\n",
+    "for scene in hy_scenes:\n",
+    "    scene_path = os.path.join(hy_base_path, scene)\n",
+    "    if not os.path.isdir(scene_path) or scene.startswith('.'):\n",
+    "        continue\n",
+    "    files = os.listdir(scene_path)\n",
+    "    file_types = {}\n",
+    "    for file in files:\n",
+    "        ext = os.path.splitext(file)[1]\n",
+    "        file_types[ext] = file_types.get(ext, 0) + 1\n",
+    "    print(f\"Scene: {scene}, Number of files: {len(files)}, File types: {file_types}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "6444b695",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'V4_linear': ['/Users/dsl/Desktop/Causal3D_Dataset/Code/../Hypothetic/V4_linear/tabular.csv'],\n",
+       " 'V4_v_structure_linear': ['/Users/dsl/Desktop/Causal3D_Dataset/Code/../Hypothetic/V4_v_structure_linear/tabular.csv'],\n",
+       " 'V3_fully_connected_linear': ['/Users/dsl/Desktop/Causal3D_Dataset/Code/../Hypothetic/V3_fully_connected_linear/tabular.csv'],\n",
+       " 'V2_linear': ['/Users/dsl/Desktop/Causal3D_Dataset/Code/../Hypothetic/V2_linear/tabular.csv'],\n",
+       " 'V2_nonlinear': ['/Users/dsl/Desktop/Causal3D_Dataset/Code/../Hypothetic/V2_nonlinear/tabular.csv'],\n",
+       " 'V5_linear': ['/Users/dsl/Desktop/Causal3D_Dataset/Code/../Hypothetic/V5_linear/tabular.csv'],\n",
+       " 'V3_v_structure_nonlinear': ['/Users/dsl/Desktop/Causal3D_Dataset/Code/../Hypothetic/V3_v_structure_nonlinear/tabular.csv'],\n",
+       " 'V3_v_structure_linear': ['/Users/dsl/Desktop/Causal3D_Dataset/Code/../Hypothetic/V3_v_structure_linear/tabular.csv'],\n",
+       " 'V5_v_structure_nonlinear': ['/Users/dsl/Desktop/Causal3D_Dataset/Code/../Hypothetic/V5_v_structure_nonlinear/tabular.csv'],\n",
+       " 'V5_v_structure_linear': ['/Users/dsl/Desktop/Causal3D_Dataset/Code/../Hypothetic/V5_v_structure_linear/tabular.csv'],\n",
+       " 'V4_v_strcuture_nonlinear': ['/Users/dsl/Desktop/Causal3D_Dataset/Code/../Hypothetic/V4_v_strcuture_nonlinear/tabular.csv']}"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hy_scene_csv_paths = {}\n",
+    "for scene in hy_scenes:\n",
+    "    scene_folder = os.path.join(hy_base_path, scene)\n",
+    "    if not os.path.isdir(scene_folder) or scene.startswith('.'):\n",
+    "        continue\n",
+    "    csvs = [f for f in os.listdir(scene_folder) if f.endswith('.csv')]\n",
+    "    hy_scene_csv_paths[scene] = [os.path.join(scene_folder, f) for f in csvs]\n",
+    "hy_scene_csv_paths"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "4bc1be73",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Hypothetic Scene: V4_linear, CSV: tabular.csv\n",
+      "Columns: ['volume_ball', 'height_cuboid', 'base_area_cuboid', 'base_area_cone', 'imgs']\n",
+      "\n",
+      "Hypothetic Scene: V4_v_structure_linear, CSV: tabular.csv\n",
+      "Columns: ['volumn_ball', 'height_cuboid', 'base_area_cuboid', 'base_area_cone', 'imgs']\n",
+      "\n",
+      "Hypothetic Scene: V3_fully_connected_linear, CSV: tabular.csv\n",
+      "Columns: ['iter', 'volume_ball', 'height_of_cuboid', 'base_area_cone', 'img_path']\n",
+      "\n",
+      "Hypothetic Scene: V2_linear, CSV: tabular.csv\n",
+      "Columns: ['iter', 'volume_ball', 'r_ball', 'volume_cube', 'edge_cube', 'img_path']\n",
+      "\n",
+      "Hypothetic Scene: V2_nonlinear, CSV: tabular.csv\n",
+      "Columns: ['iter', 'volume_ball', 'r_ball', 'scaled_volume_ball', 'volume_cube', 'edge_cube', 'img_path']\n",
+      "\n",
+      "Hypothetic Scene: V5_linear, CSV: tabular.csv\n",
+      "Columns: ['volumn_ball', 'height_cuboid', 'base_area_cuboid', 'base_area_cone', 'height_cone', 'imgs']\n",
+      "\n",
+      "Hypothetic Scene: V3_v_structure_nonlinear, CSV: tabular.csv\n",
+      "Columns: ['volume_ball', 'scaled_volume_ball', 'radius of ball', 'height_cylinder', 'radius of cone', 'basal_area_cone', 'imgs']\n",
+      "\n",
+      "Hypothetic Scene: V3_v_structure_linear, CSV: tabular.csv\n",
+      "Columns: ['volume_ball', 'height_cylinder', 'basal_area_cone', 'imgs', 'Unnamed: 4']\n",
+      "\n",
+      "Hypothetic Scene: V5_v_structure_nonlinear, CSV: tabular.csv\n",
+      "Columns: ['volumn_ball', 'scaled_volumn_ball', 'height_cuboid', 'base_area_cuboid', 'scaled_base_area_cuboid', 'base_area_cone', 'scaled_base_area_cone', 'height_cone', 'imgs']\n",
+      "\n",
+      "Hypothetic Scene: V5_v_structure_linear, CSV: tabular.csv\n",
+      "Columns: ['volumn_ball', 'height_cuboid', 'base_area_cuboid', 'base_area_cone', 'height_cone', 'imgs']\n",
+      "\n",
+      "Hypothetic Scene: V4_v_strcuture_nonlinear, CSV: tabular.csv\n",
+      "Columns: ['volumn_ball', 'scaled_volumn_ball', 'height_cuboid', 'base_area_cuboid', 'scaled_base_area_cuboid', 'base_area_cone', 'imgs']\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "for scene, csv_paths in hy_scene_csv_paths.items():\n",
+    "    for csv_file in csv_paths:\n",
+    "        df = pd.read_csv(csv_file, nrows=0)\n",
+    "        print(f\"Hypothetic Scene: {scene}, CSV: {os.path.basename(csv_file)}\")\n",
+    "        print(\"Columns:\", list(df.columns))\n",
+    "        print()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "fefc0d47",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['/Users/dsl/Desktop/Causal3D_Dataset/Hypothetic/V3_fully_connected_linear', '/Users/dsl/Desktop/Causal3D_Dataset/Hypothetic/V2_nonlinear', '/Users/dsl/Desktop/Causal3D_Dataset/Hypothetic/V2_linear'])"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "process_path = [\"/Users/dsl/Desktop/Causal3D_Dataset/Hypothetic/V3_fully_connected_linear\",\n",
+    "                \"/Users/dsl/Desktop/Causal3D_Dataset/Hypothetic/V2_nonlinear\",\n",
+    "                \"/Users/dsl/Desktop/Causal3D_Dataset/Hypothetic/V2_linear\"]\n",
+    "dfs_dict = {}\n",
+    "for path in process_path:\n",
+    "    csv_file = os.path.join(path, \"tabular.csv\")\n",
+    "    if os.path.exists(csv_file):\n",
+    "        df = pd.read_csv(csv_file)\n",
+    "        dfs_dict[path] = df\n",
+    "dfs_dict.keys()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "e6c53182",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DataFrame for: /Users/dsl/Desktop/Causal3D_Dataset/Hypothetic/V3_fully_connected_linear\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>iter</th>\n",
+       "      <th>volume_ball</th>\n",
+       "      <th>height_of_cuboid</th>\n",
+       "      <th>base_area_cone</th>\n",
+       "      <th>imgs</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>4.461709</td>\n",
+       "      <td>2.230855</td>\n",
+       "      <td>4.461709</td>\n",
+       "      <td>00001.png</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>4.641952</td>\n",
+       "      <td>2.320976</td>\n",
+       "      <td>4.641952</td>\n",
+       "      <td>00002.png</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>5.732580</td>\n",
+       "      <td>2.866290</td>\n",
+       "      <td>5.732580</td>\n",
+       "      <td>00003.png</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>9.686783</td>\n",
+       "      <td>4.843392</td>\n",
+       "      <td>9.686783</td>\n",
+       "      <td>00004.png</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>2.608935</td>\n",
+       "      <td>1.304468</td>\n",
+       "      <td>2.608935</td>\n",
+       "      <td>00005.png</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   iter  volume_ball  height_of_cuboid  base_area_cone       imgs\n",
+       "0     1     4.461709          2.230855        4.461709  00001.png\n",
+       "1     2     4.641952          2.320976        4.641952  00002.png\n",
+       "2     3     5.732580          2.866290        5.732580  00003.png\n",
+       "3     4     9.686783          4.843392        9.686783  00004.png\n",
+       "4     5     2.608935          1.304468        2.608935  00005.png"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shape: (10000, 5)\n",
+      "------------------------------------------------------------\n",
+      "DataFrame for: /Users/dsl/Desktop/Causal3D_Dataset/Hypothetic/V2_nonlinear\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>iter</th>\n",
+       "      <th>volume_ball</th>\n",
+       "      <th>r_ball</th>\n",
+       "      <th>scaled_volume_ball</th>\n",
+       "      <th>volume_cube</th>\n",
+       "      <th>edge_cube</th>\n",
+       "      <th>imgs</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>8.369589</td>\n",
+       "      <td>1.259520</td>\n",
+       "      <td>-0.260683</td>\n",
+       "      <td>5.797285</td>\n",
+       "      <td>1.796421</td>\n",
+       "      <td>00001.png</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>8.748098</td>\n",
+       "      <td>1.278228</td>\n",
+       "      <td>-0.201078</td>\n",
+       "      <td>5.879111</td>\n",
+       "      <td>1.804834</td>\n",
+       "      <td>00002.png</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>11.038418</td>\n",
+       "      <td>1.381251</td>\n",
+       "      <td>0.159586</td>\n",
+       "      <td>5.923759</td>\n",
+       "      <td>1.809391</td>\n",
+       "      <td>00003.png</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>19.342245</td>\n",
+       "      <td>1.665224</td>\n",
+       "      <td>1.467218</td>\n",
+       "      <td>0.620362</td>\n",
+       "      <td>0.852868</td>\n",
+       "      <td>00004.png</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>4.478764</td>\n",
+       "      <td>1.022562</td>\n",
+       "      <td>-0.873384</td>\n",
+       "      <td>3.853417</td>\n",
+       "      <td>1.567769</td>\n",
+       "      <td>00005.png</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   iter  volume_ball    r_ball  scaled_volume_ball  volume_cube  edge_cube  \\\n",
+       "0     1     8.369589  1.259520           -0.260683     5.797285   1.796421   \n",
+       "1     2     8.748098  1.278228           -0.201078     5.879111   1.804834   \n",
+       "2     3    11.038418  1.381251            0.159586     5.923759   1.809391   \n",
+       "3     4    19.342245  1.665224            1.467218     0.620362   0.852868   \n",
+       "4     5     4.478764  1.022562           -0.873384     3.853417   1.567769   \n",
+       "\n",
+       "        imgs  \n",
+       "0  00001.png  \n",
+       "1  00002.png  \n",
+       "2  00003.png  \n",
+       "3  00004.png  \n",
+       "4  00005.png  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shape: (10000, 7)\n",
+      "------------------------------------------------------------\n",
+      "DataFrame for: /Users/dsl/Desktop/Causal3D_Dataset/Hypothetic/V2_linear\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>iter</th>\n",
+       "      <th>volume_ball</th>\n",
+       "      <th>r_ball</th>\n",
+       "      <th>volume_cube</th>\n",
+       "      <th>edge_cube</th>\n",
+       "      <th>imgs</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>7.118523</td>\n",
+       "      <td>1.193348</td>\n",
+       "      <td>10.677784</td>\n",
+       "      <td>2.202049</td>\n",
+       "      <td>00001.png</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>7.440114</td>\n",
+       "      <td>1.211054</td>\n",
+       "      <td>11.160170</td>\n",
+       "      <td>2.234723</td>\n",
+       "      <td>00002.png</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>9.386024</td>\n",
+       "      <td>1.308572</td>\n",
+       "      <td>14.079037</td>\n",
+       "      <td>2.414669</td>\n",
+       "      <td>00003.png</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>16.441156</td>\n",
+       "      <td>1.577422</td>\n",
+       "      <td>24.661734</td>\n",
+       "      <td>2.910770</td>\n",
+       "      <td>00004.png</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>3.812784</td>\n",
+       "      <td>0.969136</td>\n",
+       "      <td>5.719176</td>\n",
+       "      <td>1.788317</td>\n",
+       "      <td>00005.png</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   iter  volume_ball    r_ball  volume_cube  edge_cube       imgs\n",
+       "0     1     7.118523  1.193348    10.677784   2.202049  00001.png\n",
+       "1     2     7.440114  1.211054    11.160170   2.234723  00002.png\n",
+       "2     3     9.386024  1.308572    14.079037   2.414669  00003.png\n",
+       "3     4    16.441156  1.577422    24.661734   2.910770  00004.png\n",
+       "4     5     3.812784  0.969136     5.719176   1.788317  00005.png"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shape: (10000, 6)\n",
+      "------------------------------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "for path, df in dfs_dict.items():\n",
+    "    print(f\"DataFrame for: {path}\")\n",
+    "    display(df.head())\n",
+    "    print(f\"Shape: {df.shape}\")\n",
+    "    print(\"-\" * 60)\n",
+    "    # if 'img_path' in df.columns:\n",
+    "    #     df = df.rename(columns={'img_path': 'imgs'})\n",
+    "    #     # Update the original CSV file\n",
+    "    #     csv_file = os.path.join(path, \"tabular.csv\")\n",
+    "    #     df.to_csv(csv_file, index=False)\n",
+    "    #     print(f\"Updated column name and saved: {csv_file}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "835862a9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'V4_linear': ['/Users/dsl/Desktop/Causal3D_Dataset/Code/../Hypothetic/V4_linear/tabular.csv'],\n",
+       " 'V4_v_structure_linear': ['/Users/dsl/Desktop/Causal3D_Dataset/Code/../Hypothetic/V4_v_structure_linear/tabular.csv'],\n",
+       " 'V3_fully_connected_linear': ['/Users/dsl/Desktop/Causal3D_Dataset/Code/../Hypothetic/V3_fully_connected_linear/tabular.csv'],\n",
+       " 'V2_linear': ['/Users/dsl/Desktop/Causal3D_Dataset/Code/../Hypothetic/V2_linear/tabular.csv'],\n",
+       " 'V2_nonlinear': ['/Users/dsl/Desktop/Causal3D_Dataset/Code/../Hypothetic/V2_nonlinear/tabular.csv'],\n",
+       " 'V5_linear': ['/Users/dsl/Desktop/Causal3D_Dataset/Code/../Hypothetic/V5_linear/tabular.csv'],\n",
+       " 'V3_v_structure_nonlinear': ['/Users/dsl/Desktop/Causal3D_Dataset/Code/../Hypothetic/V3_v_structure_nonlinear/tabular.csv'],\n",
+       " 'V3_v_structure_linear': ['/Users/dsl/Desktop/Causal3D_Dataset/Code/../Hypothetic/V3_v_structure_linear/tabular.csv'],\n",
+       " 'V5_v_structure_nonlinear': ['/Users/dsl/Desktop/Causal3D_Dataset/Code/../Hypothetic/V5_v_structure_nonlinear/tabular.csv'],\n",
+       " 'V5_v_structure_linear': ['/Users/dsl/Desktop/Causal3D_Dataset/Code/../Hypothetic/V5_v_structure_linear/tabular.csv'],\n",
+       " 'V4_v_strcuture_nonlinear': ['/Users/dsl/Desktop/Causal3D_Dataset/Code/../Hypothetic/V4_v_strcuture_nonlinear/tabular.csv']}"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hy_scene_csv_paths"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "f5d085eb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Scene: V4_linear, all images exist.\n",
+      "Scene: V4_v_structure_linear, all images exist.\n",
+      "Scene: V3_fully_connected_linear, all images exist.\n",
+      "Scene: V2_linear, all images exist.\n",
+      "Scene: V2_nonlinear, all images exist.\n",
+      "Scene: V5_linear, all images exist.\n",
+      "Scene: V3_v_structure_nonlinear, all images exist.\n",
+      "Scene: V3_v_structure_linear, all images exist.\n",
+      "Scene: V5_v_structure_nonlinear, all images exist.\n",
+      "Scene: V5_v_structure_linear, all images exist.\n",
+      "Scene: V4_v_strcuture_nonlinear, all images exist.\n"
+     ]
+    }
+   ],
+   "source": [
+    "for scene, csv_paths in hy_scene_csv_paths.items():\n",
+    "    for csv_file in csv_paths:\n",
+    "        df = pd.read_csv(csv_file)\n",
+    "        img_col = 'imgs' if 'imgs' in df.columns else 'img_path'\n",
+    "        img_dir = os.path.dirname(csv_file)\n",
+    "        missing_imgs = []\n",
+    "        for img_file in df[img_col]:\n",
+    "            img_path = os.path.join(img_dir, img_file)\n",
+    "            if not os.path.exists(img_path):\n",
+    "                missing_imgs.append(img_file)\n",
+    "        if missing_imgs:\n",
+    "            print(f\"Scene: {scene}, Missing images: {len(missing_imgs)}\")\n",
+    "            print(missing_imgs[:10])  # show up to 10 missing images\n",
+    "        else:\n",
+    "            print(f\"Scene: {scene}, all images exist.\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

Hypothetical_V2_linear.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:821ea1b7005bf602b429a62abf7d7501a6345b24050c0c68413ac2af5f55d805
+size 797636768

Hypothetical_V2_nonlinear.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7b860d2e64a140642e8de87a2e295ca16963539a75474110975f64f1d0e4df38
+size 800066916

Hypothetical_V3_fully_connected_linear.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8719d474608f9f15642cf7ddda795d0bacbc701fd49ed140d68923d4e4e1eb59
+size 822351161

Hypothetical_V3_v_structure_linear.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f3a9ca82dd5760fb7729aa022f28836ce12c5e4cfb69c037fdf2540a7a9ca2fe
+size 779911922

Hypothetical_V3_v_structure_nonlinear.zip

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+version https://git-lfs.github.com/spec/v1
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+size 782501114

Hypothetical_V4_linear.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5948e9a9a4331113e81893c0b194af1174f28968f270238ef1192f425bd4997a
+size 825828410

Hypothetical_V4_v_strcuture_nonlinear.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5a6372cc5adcc3bf4d122e6ae05fee13fa4fa8fcb177d93af36f787b1ee55067
+size 811083458

Hypothetical_V4_v_structure_linear.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2041f4fa92c7b57326babf9549a7bda94e9a0934f48bf33db645f1a1598f894f
+size 807961243

Hypothetical_V5_linear.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:87c7ccb4a6492eae84b609193984805538adbd61f1b904d0eb08290f3f2f011f
+size 809089702

Hypothetical_V5_v_structure_linear.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:417818cf5602914ef06dae749761a72dc9468b1e512d948067e4b0a3c8eadf1a
+size 810077103

Hypothetical_V5_v_structure_nonlinear.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0c9f66d78b5e801976cf419986ffe5f62c57150501f71b2116903fd86f0266d5
+size 810083426

Real_Convex_len.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e39b46a68b4ba5a4252af37a97abfc658dd637efddffc632d70cd11a2cb53f9a
+size 1266758341

Real_Magnet.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5ba9b5da9cdab0bf6712e78c452f9d68f149a0c6d7e9dee7275cfe9d5718a49a
+size 133242122

Real_Parabola.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:044dea8695e172d1a76b4e0023584aef358c3315ba9f9a598671dbf33ca08c0d
+size 1383325161

Real_Pendulum.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:66ce6473481ee775e01a9015bfd9e9a7d6c872cd7fbcb7ae5cef64f7da108e80
+size 712987799

Real_Reflection.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:df752e9f8e50b9e6232cd2b37cf6b649021dedd6856d636ac610780b5367dabc
+size 790883783

Real_Seesaw.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a212e1e66843e607687ee175289bb8c274eca08b246f227f1389f2937e3d7ced
+size 787423731

Real_Spring.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0c8c55d52c242e38f593135a03571e1d37b40396cb82a4ba12dd514e30713864
+size 733517324

Real_Water_flow.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:dac569760cf2b057620e7888d39dae504fcdb745f4802fbefe1eb4c2685c1b9a
+size 733356836

zip_scene.py

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+import os
+import zipfile
+from pathlib import Path
+
+def zip_folder(folder_path, output_path):
+    """
+    压缩单个文件夹
+    """
+    folder = Path(folder_path)
+    with zipfile.ZipFile(output_path, 'w', zipfile.ZIP_DEFLATED) as zipf:
+        for file in folder.rglob('*'):
+            zipf.write(file, file.relative_to(folder.parent))
+    print(f"✅ Zipped {folder} -> {output_path}")
+
+def zip_all_scenes(root_dir):
+    """
+    扫描 Real, Hypothetical, Multi_View 文件夹，自动打包每个 scene
+    """
+    root = Path(root_dir)
+    categories = ["Real", "Hypothetical", "Multi_View"]
+
+    for category in categories:
+        category_path = root / category
+        if not category_path.exists():
+            print(f"⚠️  Skip {category}: not found")
+            continue
+
+        for scene in category_path.iterdir():
+            if scene.is_dir():
+                output_zip = root / f"{category}_{scene.name}.zip"
+                zip_folder(scene, output_zip)
+
+if __name__ == "__main__":
+    # 修改为你的根目录
+    zip_all_scenes(".")
+
+    print("✅ All scenes zipped successfully.")