metadata
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, 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 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) anduniform/surface/groundtruth, each anArray2D[262144, 4]float32 tensor ([x, y, z, sdf]per point — see File format below).The
allconfig globs every category's Parquet, soload_dataset(..., "all")streams all 50 categories together. The split id lists undermeta/(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 aggregatedall/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 = 262,144points 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 inmeta/<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 bymodel_id.meta/all/labels.json—{"category_to_filename": {...}, "filename_to_category": {...}}mapping each category to its model ids and vice versa.
Usage
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:
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):
- Convert the mesh to a watertight manifold (via
point_cloud_utils). - Normalize into the unit cube centered at the origin (scale
0.98of the max vertex distance). - Sample
uniform,surface(+noise), andgroundtruthpoint sets. - 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".
@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. Use it for non-commercial research only, and cite ShapeNet:
@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}
}