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

ShapeNetSDF/
├── <category>/                     # 51 category folders (chair, table, airplane, ...)
│   ├── uniform/<model_id>.npy      # [262144, 4]  points uniform in [-1,1]³ + SDF
│   ├── surface/<model_id>.npy      # [262144, 4]  near-surface points (+noise) + SDF
│   ├── groundtruth/<model_id>.npy  # [262144, 4]  exact surface points (SDF ≈ 0)
│   ├── train.txt                   # model ids, ~80% split
│   ├── val.txt                     # model ids, ~10% split
│   └── test.txt                    # model ids, ~10% split
├── all/                            # aggregated view across all categories
│   ├── uniform/ surface/ groundtruth/
│   ├── train.txt / val.txt / test.txt   # global splits (all categories)
│   ├── 10k10c.txt / 5k10c.txt / 5k5c.txt / 100_5c.txt   # curated subsets
│   └── labels.json                 # category ↔ model_id mappings
└── README.md

• 51 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

Every .npy file is a float32 array of shape [N, 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

Splits are plain-text files with one model_id per line. Per-category splits live in each category folder; global splits and curated subsets live in all/:

• train.txt / val.txt / test.txt — ~80% / 10% / 10% split (seed 42).
• 10k10c.txt, 5k10c.txt, 5k5c.txt, 100_5c.txt — curated subsets (<num_models><num_categories>c, e.g. 5k5c = 5,000 models across 5 categories) for quick experiments.
• labels.json — {"category_to_filename": {...}, "filename_to_category": {...}} mapping each category to its model ids and vice versa.

Usage

import numpy as np
from huggingface_hub import hf_hub_download

# Download a single shape's uniform samples.
path = hf_hub_download(
    repo_id="EPFL-IVRL/ShapeNetSDF",
    repo_type="dataset",
    filename="chair/uniform/209994649e7fdf052ff84f70e18e9c53.npy",
)
points = np.load(path)          # [262144, 4] -> [x, y, z, sdf]
xyz, sdf = points[:, :3], points[:, 3]

Or download a whole category / split locally:

from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="EPFL-IVRL/ShapeNetSDF",
    repo_type="dataset",
    allow_patterns=["chair/**", "all/train.txt", "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 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}
}