BumsooKim00/nisr-dataset

NISR Dataset — Neural Inverse Sound Rendering

Dataset for training a model that predicts natural frequencies and 3D mode shapes from an impact sound recording and a 3D mesh.

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Changelog

Version	Date	Objects	Description
v1.0	2026-01	100	Initial release — ObjectFolder-Real (100 everyday objects)
v2.0	2026-06-20	1100	Extended with ObjectFolder 2.0 (+1000 objects, obj_id 101–1100)

Current version: v2.0 (2026-06-20) obj_id 1–100: ObjectFolder-Real · obj_id 101–1100: ObjectFolder 2.0

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Derived from ObjectFolder-Real (100 everyday objects) and ObjectFolder 2.0 (1000 objects). Modal data generated via FEM (LOBPCG) simulation with 8 material configurations per object.

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Task

Input:  impact sound wav  +  3D mesh (voxel 32³)  +  object size L  +  material (E, ρ, ν)
Output: natural frequencies (k,)  +  3D mode shapes (B, 3, k)

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Dataset Statistics

Objects	1100 (ObjectFolder-Real 100 + ObjectFolder 2.0 1000)
Materials per object	8
Modes per sample	up to 20
Audio	2.0 sec @ 44,100 Hz
Total samples	8800 (1100 objects × 8 materials)
Total size	~22 GB

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Structure

training_dataset/{obj_id}/
├── voxel.npz               ← 3D voxel grid (32³), shared across materials
├── feat/feat_{mat}.npz     ← training labels (freqs, mode shapes)
└── wav/sound_{mat}.wav     ← impact sound (rendered)

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Splits

Train/val/test split is not yet defined. All samples are currently unsplit. Split assignment will be added in a future release.

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Download

Option 1 — Python (recommended)

from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="BumsooKim00/nisr-dataset",
    repo_type="dataset",
    local_dir="./nisr-dataset",
)

Option 2 — git clone

git lfs install
git clone https://huggingface.co/datasets/BumsooKim00/nisr-dataset

Option 3 — wget (single file)

# voxel
wget https://huggingface.co/datasets/BumsooKim00/nisr-dataset/resolve/main/training_dataset/1/voxel.npz

# feat
wget https://huggingface.co/datasets/BumsooKim00/nisr-dataset/resolve/main/training_dataset/1/feat/feat_Ceramic.npz

# wav
wget https://huggingface.co/datasets/BumsooKim00/nisr-dataset/resolve/main/training_dataset/1/wav/sound_Ceramic.wav

No login required (public repo). Python: pip install huggingface_hub

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Loading

import numpy as np
import scipy.io.wavfile as wav

obj_id = 1
mat    = "Ceramic"
base   = f"training_dataset/{obj_id}"

# audio input
fs, audio = wav.read(f"{base}/wav/sound_{mat}.wav")
audio = audio.astype(np.float32) / 32767.0   # int16 → float32

# mesh input
voxel = np.load(f"{base}/voxel.npz")["voxel"]       # (32, 32, 32)

# labels
feat     = np.load(f"{base}/feat/feat_{mat}.npz")
freqs    = feat["freqs"]      # (k,)      natural frequencies in Hz
feats_in = feat["feats_in"]   # (B, 3, k) mode shapes on boundary voxels
coords   = feat["coords"]     # (B, 3)    boundary voxel coordinates
surface  = feat["surface"]    # (B, 6)    surface normal encoding

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Materials

Material	ρ (kg/m³)	E (Pa)	ν
Ceramic	2700	7.2e10	0.19
Glass	2600	6.2e10	0.20
Wood	750	1.1e10	0.25
Plastic	1070	1.4e9	0.35
Iron	8000	2.1e11	0.28
Polycarbonate	1190	2.4e9	0.37
Steel	7850	2.0e11	0.29
Tin	7265	5.0e10	0.325

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Generation

FEM modal analysis (LOBPCG) → modal feature extraction → modal sound synthesis.

See GENERATE.md for the full pipeline.

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License

CC BY 4.0 Mesh data from ObjectFolder-Real — original license applies to source meshes.