Overview

Objaverse++ extends Objaverse by adding neural field representations (NeRF, 3D Gaussian Splatting) and richer annotations for simulation and high‑fidelity rendering. Official sources:

Neural Representations

NeRF: learns a continuous radiance field; excellent detail, higher training cost
3DGS: many Gaussian splats; very fast rendering, great for interactive scenes

Background reading: NeRF (Mildenhall et al.) • 3DGS

System Requirements

GPU: 24GB VRAM recommended for larger scenes (e.g., RTX 4090/A6000)
RAM: 64–128GB for parallel preprocessing
Storage: fast NVMe for neural caches; offload archives to NAS
Software: PyTorch + CUDA; pin versions per project for reproducibility

Python Workflow

pip install objaverse-plus torch torchvision import objaverse_plus as oplus uids = ["n1b2c3","n4d5e6"] neural_objs = oplus.load_with_neural(uids=uids, download_dir="./neural_data") # Example: build a simple dataset wrapper dataset = oplus.NeuralDataset(neural_objs)

Ensure CUDA versions match your PyTorch build. Use mixed precision where it helps.

Performance Tips

Profile Gaussian counts and prune for target FPS
Use caching to avoid redundant conversions
Benchmark view sampling with a fixed camera path

Objaverse++ vs Alternatives

Compare ++ to XL and other datasets on the Top 5 Dataset Comparison.

FAQ

Is every object in ++ a neural field?

No; ++ adds many neural fields but not all objects have NeRF/3DGS versions.

Which is faster for real‑time?

3D Gaussian Splatting typically renders much faster than NeRF with competitive quality.