Add files using upload-large-folder tool

README.md

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+---
+license: other
+license_name: shapenet-license
+license_link: https://shapenet.org/terms
+task_categories:
+  - other
+tags:
+  - 3d
+  - sdf
+  - signed-distance-field
+  - shapenet
+  - point-cloud
+  - neural-fields
+  - implicit-neural-representation
+pretty_name: ShapeNetSDF
+size_categories:
+  - 100B<n<1T
+configs:
+  - config_name: airplane
+    data_files:
+      - split: train
+        path: data/airplane/train-*.parquet
+      - split: val
+        path: data/airplane/val-*.parquet
+      - split: test
+        path: data/airplane/test-*.parquet
+  - config_name: bag
+    data_files:
+      - split: train
+        path: data/bag/train-*.parquet
+      - split: val
+        path: data/bag/val-*.parquet
+      - split: test
+        path: data/bag/test-*.parquet
+  - config_name: basket
+    data_files:
+      - split: train
+        path: data/basket/train-*.parquet
+      - split: val
+        path: data/basket/val-*.parquet
+      - split: test
+        path: data/basket/test-*.parquet
+  - config_name: bathtub
+    data_files:
+      - split: train
+        path: data/bathtub/train-*.parquet
+      - split: val
+        path: data/bathtub/val-*.parquet
+      - split: test
+        path: data/bathtub/test-*.parquet
+  - config_name: bed
+    data_files:
+      - split: train
+        path: data/bed/train-*.parquet
+      - split: val
+        path: data/bed/val-*.parquet
+      - split: test
+        path: data/bed/test-*.parquet
+  - config_name: bench
+    data_files:
+      - split: train
+        path: data/bench/train-*.parquet
+      - split: val
+        path: data/bench/val-*.parquet
+      - split: test
+        path: data/bench/test-*.parquet
+  - config_name: birdhouse
+    data_files:
+      - split: train
+        path: data/birdhouse/train-*.parquet
+      - split: val
+        path: data/birdhouse/val-*.parquet
+      - split: test
+        path: data/birdhouse/test-*.parquet
+  - config_name: bookshelf
+    data_files:
+      - split: train
+        path: data/bookshelf/train-*.parquet
+      - split: val
+        path: data/bookshelf/val-*.parquet
+      - split: test
+        path: data/bookshelf/test-*.parquet
+  - config_name: bottle
+    data_files:
+      - split: train
+        path: data/bottle/train-*.parquet
+      - split: val
+        path: data/bottle/val-*.parquet
+      - split: test
+        path: data/bottle/test-*.parquet
+  - config_name: bowl
+    data_files:
+      - split: train
+        path: data/bowl/train-*.parquet
+      - split: val
+        path: data/bowl/val-*.parquet
+      - split: test
+        path: data/bowl/test-*.parquet
+  - config_name: bus
+    data_files:
+      - split: train
+        path: data/bus/train-*.parquet
+      - split: val
+        path: data/bus/val-*.parquet
+      - split: test
+        path: data/bus/test-*.parquet
+  - config_name: cabinet
+    data_files:
+      - split: train
+        path: data/cabinet/train-*.parquet
+      - split: val
+        path: data/cabinet/val-*.parquet
+      - split: test
+        path: data/cabinet/test-*.parquet
+  - config_name: camera
+    data_files:
+      - split: train
+        path: data/camera/train-*.parquet
+      - split: val
+        path: data/camera/val-*.parquet
+      - split: test
+        path: data/camera/test-*.parquet
+  - config_name: can
+    data_files:
+      - split: train
+        path: data/can/train-*.parquet
+      - split: val
+        path: data/can/val-*.parquet
+      - split: test
+        path: data/can/test-*.parquet
+  - config_name: cap
+    data_files:
+      - split: train
+        path: data/cap/train-*.parquet
+      - split: val
+        path: data/cap/val-*.parquet
+      - split: test
+        path: data/cap/test-*.parquet
+  - config_name: car
+    data_files:
+      - split: train
+        path: data/car/train-*.parquet
+      - split: val
+        path: data/car/val-*.parquet
+      - split: test
+        path: data/car/test-*.parquet
+  - config_name: chair
+    data_files:
+      - split: train
+        path: data/chair/train-*.parquet
+      - split: val
+        path: data/chair/val-*.parquet
+      - split: test
+        path: data/chair/test-*.parquet
+  - config_name: dishwasher
+    data_files:
+      - split: train
+        path: data/dishwasher/train-*.parquet
+      - split: val
+        path: data/dishwasher/val-*.parquet
+      - split: test
+        path: data/dishwasher/test-*.parquet
+  - config_name: display
+    data_files:
+      - split: train
+        path: data/display/train-*.parquet
+      - split: val
+        path: data/display/val-*.parquet
+      - split: test
+        path: data/display/test-*.parquet
+  - config_name: earphone
+    data_files:
+      - split: train
+        path: data/earphone/train-*.parquet
+      - split: val
+        path: data/earphone/val-*.parquet
+      - split: test
+        path: data/earphone/test-*.parquet
+  - config_name: faucet
+    data_files:
+      - split: train
+        path: data/faucet/train-*.parquet
+      - split: val
+        path: data/faucet/val-*.parquet
+      - split: test
+        path: data/faucet/test-*.parquet
+  - config_name: file_cabinet
+    data_files:
+      - split: train
+        path: data/file_cabinet/train-*.parquet
+      - split: val
+        path: data/file_cabinet/val-*.parquet
+      - split: test
+        path: data/file_cabinet/test-*.parquet
+  - config_name: guitar
+    data_files:
+      - split: train
+        path: data/guitar/train-*.parquet
+      - split: val
+        path: data/guitar/val-*.parquet
+      - split: test
+        path: data/guitar/test-*.parquet
+  - config_name: jar
+    data_files:
+      - split: train
+        path: data/jar/train-*.parquet
+      - split: val
+        path: data/jar/val-*.parquet
+      - split: test
+        path: data/jar/test-*.parquet
+  - config_name: keyboard
+    data_files:
+      - split: train
+        path: data/keyboard/train-*.parquet
+      - split: val
+        path: data/keyboard/val-*.parquet
+      - split: test
+        path: data/keyboard/test-*.parquet
+  - config_name: knife
+    data_files:
+      - split: train
+        path: data/knife/train-*.parquet
+      - split: val
+        path: data/knife/val-*.parquet
+      - split: test
+        path: data/knife/test-*.parquet
+  - config_name: lamp
+    data_files:
+      - split: train
+        path: data/lamp/train-*.parquet
+      - split: val
+        path: data/lamp/val-*.parquet
+      - split: test
+        path: data/lamp/test-*.parquet
+  - config_name: laptop
+    data_files:
+      - split: train
+        path: data/laptop/train-*.parquet
+      - split: val
+        path: data/laptop/val-*.parquet
+      - split: test
+        path: data/laptop/test-*.parquet
+  - config_name: loudspeaker
+    data_files:
+      - split: train
+        path: data/loudspeaker/train-*.parquet
+      - split: val
+        path: data/loudspeaker/val-*.parquet
+      - split: test
+        path: data/loudspeaker/test-*.parquet
+  - config_name: mailbox
+    data_files:
+      - split: train
+        path: data/mailbox/train-*.parquet
+      - split: val
+        path: data/mailbox/val-*.parquet
+      - split: test
+        path: data/mailbox/test-*.parquet
+  - config_name: microphone
+    data_files:
+      - split: train
+        path: data/microphone/train-*.parquet
+      - split: val
+        path: data/microphone/val-*.parquet
+      - split: test
+        path: data/microphone/test-*.parquet
+  - config_name: motorbike
+    data_files:
+      - split: train
+        path: data/motorbike/train-*.parquet
+      - split: val
+        path: data/motorbike/val-*.parquet
+      - split: test
+        path: data/motorbike/test-*.parquet
+  - config_name: mug
+    data_files:
+      - split: train
+        path: data/mug/train-*.parquet
+      - split: val
+        path: data/mug/val-*.parquet
+      - split: test
+        path: data/mug/test-*.parquet
+  - config_name: piano
+    data_files:
+      - split: train
+        path: data/piano/train-*.parquet
+      - split: val
+        path: data/piano/val-*.parquet
+      - split: test
+        path: data/piano/test-*.parquet
+  - config_name: pillow
+    data_files:
+      - split: train
+        path: data/pillow/train-*.parquet
+      - split: val
+        path: data/pillow/val-*.parquet
+      - split: test
+        path: data/pillow/test-*.parquet
+  - config_name: pistol
+    data_files:
+      - split: train
+        path: data/pistol/train-*.parquet
+      - split: val
+        path: data/pistol/val-*.parquet
+      - split: test
+        path: data/pistol/test-*.parquet
+  - config_name: printer
+    data_files:
+      - split: train
+        path: data/printer/train-*.parquet
+      - split: val
+        path: data/printer/val-*.parquet
+      - split: test
+        path: data/printer/test-*.parquet
+  - config_name: remote
+    data_files:
+      - split: train
+        path: data/remote/train-*.parquet
+      - split: val
+        path: data/remote/val-*.parquet
+      - split: test
+        path: data/remote/test-*.parquet
+  - config_name: rifle
+    data_files:
+      - split: train
+        path: data/rifle/train-*.parquet
+      - split: val
+        path: data/rifle/val-*.parquet
+      - split: test
+        path: data/rifle/test-*.parquet
+  - config_name: rocket
+    data_files:
+      - split: train
+        path: data/rocket/train-*.parquet
+      - split: val
+        path: data/rocket/val-*.parquet
+      - split: test
+        path: data/rocket/test-*.parquet
+  - config_name: skateboard
+    data_files:
+      - split: train
+        path: data/skateboard/train-*.parquet
+      - split: val
+        path: data/skateboard/val-*.parquet
+      - split: test
+        path: data/skateboard/test-*.parquet
+  - config_name: sofa
+    data_files:
+      - split: train
+        path: data/sofa/train-*.parquet
+      - split: val
+        path: data/sofa/val-*.parquet
+      - split: test
+        path: data/sofa/test-*.parquet
+  - config_name: stove
+    data_files:
+      - split: train
+        path: data/stove/train-*.parquet
+      - split: val
+        path: data/stove/val-*.parquet
+      - split: test
+        path: data/stove/test-*.parquet
+  - config_name: table
+    data_files:
+      - split: train
+        path: data/table/train-*.parquet
+      - split: val
+        path: data/table/val-*.parquet
+      - split: test
+        path: data/table/test-*.parquet
+  - config_name: telephone
+    data_files:
+      - split: train
+        path: data/telephone/train-*.parquet
+      - split: val
+        path: data/telephone/val-*.parquet
+      - split: test
+        path: data/telephone/test-*.parquet
+  - config_name: tower
+    data_files:
+      - split: train
+        path: data/tower/train-*.parquet
+      - split: val
+        path: data/tower/val-*.parquet
+      - split: test
+        path: data/tower/test-*.parquet
+  - config_name: train
+    data_files:
+      - split: train
+        path: data/train/train-*.parquet
+      - split: val
+        path: data/train/val-*.parquet
+      - split: test
+        path: data/train/test-*.parquet
+  - config_name: trash_bin
+    data_files:
+      - split: train
+        path: data/trash_bin/train-*.parquet
+      - split: val
+        path: data/trash_bin/val-*.parquet
+      - split: test
+        path: data/trash_bin/test-*.parquet
+  - config_name: washer
+    data_files:
+      - split: train
+        path: data/washer/train-*.parquet
+      - split: val
+        path: data/washer/val-*.parquet
+      - split: test
+        path: data/washer/test-*.parquet
+  - config_name: watercraft
+    data_files:
+      - split: train
+        path: data/watercraft/train-*.parquet
+      - split: val
+        path: data/watercraft/val-*.parquet
+      - split: test
+        path: data/watercraft/test-*.parquet
+  - config_name: all
+    data_files:
+      - split: train
+        path: data/*/train-*.parquet
+      - split: val
+        path: data/*/val-*.parquet
+      - split: test
+        path: data/*/test-*.parquet
+---
+
+# ShapeNetSDF
+
+Signed Distance Field (SDF) point samples derived from
+[ShapeNet Core](https://shapenet.org), for training and evaluating implicit
+neural representations / neural fields on 3D shapes.
+
+Each shape is converted into a watertight manifold, normalized into the unit
+cube `[-1, 1]³`, and sampled into three point sets (`uniform`, `surface`,
+`groundtruth`), each stored as a `[N, 4]` `float32` array of `[x, y, z, sdf]`.
+
+This dataset is produced by `process_shapenet_to_sdf.py` from the
+[wsr.pytorch](https://github.com/IVRL/wsr.pytorch) neural-field codebase.
+
+## Dataset structure
+
+The dataset is stored as **sharded Parquet** (one config per category, with
+`train` / `val` / `test` splits). Each row is one shape; the three point sets
+(`uniform`, `surface`, `groundtruth`) are stored as fixed-shape `[262144, 4]`
+float32 tensors in their own columns.
+
+```
+ShapeNetSDF/
+├── data/                           # heavy data: sharded parquet, one folder per category
+│   └── <category>/                 # 50 category configs (chair, table, airplane, ...)
+│       ├── train-NNNNN-of-NNNNN.parquet   # rows: model_id, uniform, surface, groundtruth
+│       ├── val-NNNNN-of-NNNNN.parquet
+│       └── test-NNNNN-of-NNNNN.parquet
+├── meta/                           # lightweight metadata (split id lists, labels, subsets)
+│   ├── <category>/                 # per-category split id lists
+│   │   └── train.txt / val.txt / test.txt
+│   └── all/                        # global splits + curated subsets + labels
+│       ├── train.txt / val.txt / test.txt   # global splits (all categories)
+│       ├── 10k10c.txt / 5k10c.txt / 5k5c.txt / 100_5c.txt   # curated subsets (model-id lists)
+│       └── labels.json             # category ↔ model_id mappings
+└── README.md
+```
+
+- Each Parquet **row** has columns `model_id` (string) and `uniform` /
+  `surface` / `groundtruth`, each an `Array2D[262144, 4]` float32 tensor
+  (`[x, y, z, sdf]` per point — see *File format* below).
+- The `all` config globs every category's Parquet, so
+  `load_dataset(..., "all")` streams all 50 categories together. The split id
+  lists under `meta/` (and curated subsets) remain for selecting specific model ids.
+
+- **50 categories**: `airplane, bag, basket, bathtub, bed, bench, birdhouse,
+  bookshelf, bottle, bowl, bus, cabinet, camera, can, cap, car, chair,
+  dishwasher, display, earphone, faucet, file_cabinet, guitar, jar, keyboard,
+  knife, lamp, laptop, loudspeaker, mailbox, microphone, motorbike, mug, piano,
+  pillow, pistol, printer, remote, rifle, rocket, skateboard, sofa, stove,
+  table, telephone, tower, train, trash_bin, washer, watercraft` (plus the
+  aggregated `all/` folder).
+- **Total size**: ~512 GB.
+
+### File format
+
+Each point set (`uniform` / `surface` / `groundtruth`) is a `float32` array of
+shape `[262144, 4]`:
+
+| column | meaning | range |
+| ------ | ------- | ----- |
+| 0 (`x`) | x coordinate | `[-1, 1]` |
+| 1 (`y`) | y coordinate | `[-1, 1]` |
+| 2 (`z`) | z coordinate | `[-1, 1]` |
+| 3 (`sdf`) | signed distance to the surface | negative inside, positive outside |
+
+- **`uniform`** — `N = 64³ = 262,144` points sampled uniformly in `[-1, 1]³`,
+  with their exact signed distance. Use these to supervise the field in free
+  space.
+- **`surface`** — points sampled on the mesh surface with a small Gaussian
+  perturbation (noise scale ≈ `0.02`), so SDF values are close to (but not
+  exactly) zero. Intended for training near the surface.
+- **`groundtruth`** — exact on-surface points (SDF ≈ `0`), with no noise.
+  Intended for evaluation (e.g. surface reconstruction / Chamfer / IoU).
+
+### Splits
+
+Each category config has `train` / `val` / `test` splits (~80% / 10% / 10%,
+seed `42`), encoded as the Parquet partitions. Plain-text `model_id` lists under
+`meta/` remain for global splits and curated subsets:
+
+- `meta/all/train.txt` / `val.txt` / `test.txt` — global splits over all categories
+  (per-category lists live in `meta/<category>/`).
+- `meta/all/{10k10c,5k10c,5k5c,100_5c}.txt` — curated subsets
+  (`<num_models><num_categories>c`, e.g. `5k5c` = 5,000 models across 5
+  categories) for quick experiments. Filter the loaded dataset by `model_id`.
+- `meta/all/labels.json` — `{"category_to_filename": {...}, "filename_to_category": {...}}`
+  mapping each category to its model ids and vice versa.
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+# One category, one split. Array2D columns decode to numpy [262144, 4] arrays.
+ds = load_dataset("EPFL-IVRL/ShapeNetSDF", "chair", split="train").with_format("numpy")
+row = ds[0]
+xyz, sdf = row["uniform"][:, :3], row["uniform"][:, 3]   # [262144, 3], [262144]
+# row also has "surface" and "groundtruth", same shape, plus "model_id".
+
+# All categories together (streamed to avoid downloading everything at once):
+ds_all = load_dataset("EPFL-IVRL/ShapeNetSDF", "all", split="train", streaming=True)
+```
+
+Or download the Parquet files for a category locally:
+
+```python
+from huggingface_hub import snapshot_download
+
+snapshot_download(
+    repo_id="EPFL-IVRL/ShapeNetSDF",
+    repo_type="dataset",
+    allow_patterns=["data/chair/**", "meta/all/labels.json"],
+    local_dir="ShapeNetSDF",
+)
+```
+
+## How it was created
+
+For each ShapeNet Core model (`models/model_normalized.obj`):
+
+1. Convert the mesh to a watertight manifold (via `point_cloud_utils`).
+2. Normalize into the unit cube centered at the origin (scale `0.98` of the max
+   vertex distance).
+3. Sample `uniform`, `surface` (+noise), and `groundtruth` point sets.
+4. Compute the signed distance for every point and save as `[N, 4]` `float32`.
+
+Processing is deterministic (per-`model_id` seeding), so results are
+reproducible. See `neural_field/scripts/process_shapenet_to_sdf.py` for the full
+pipeline and configuration.
+
+## License & citation
+
+This dataset is created as part of the CVPR 2026 Paper "Weight Space Representation Learning
+via Neural Field Adaptation".
+
+```bibtex
+@inproceedings{yang2026wsr,
+  title     = {Weight Space Representation Learning via Neural Field Adaptation},
+  author    = {Yang, Zhuoqian and Salzmann, Mathieu and S{\"u}sstrunk, Sabine},
+  booktitle = {Proceedings of the IEEE/CVF Conference on
+               Computer Vision and Pattern Recognition (CVPR)},
+  year      = {2026}
+}
+```
+
+This dataset is derived from **ShapeNet** and is subject to the
+[ShapeNet terms of use](https://shapenet.org/terms). Use it for non-commercial
+research only, and cite ShapeNet:
+
+```bibtex
+@article{chang2015shapenet,
+  title   = {ShapeNet: An Information-Rich 3D Model Repository},
+  author  = {Chang, Angel X. and Funkhouser, Thomas and Guibas, Leonidas and
+             Hanrahan, Pat and Huang, Qixing and Li, Zimo and Savarese, Silvio
+             and Savva, Manolis and Song, Shuran and Su, Hao and Xiao, Jianxiong
+             and Yi, Li and Yu, Fisher},
+  journal = {arXiv preprint arXiv:1512.03012},
+  year    = {2015}
+}
+```

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