nerfacc

library0.5.3pypypi✓ verified 90d ago

A general NeRF acceleration toolbox that provides efficient occupancy grid-based ray marching and sampling for neural radiance fields. Current version 0.5.3, with rapid development and breaking changes between minor versions.

ai-ml data

pip install nerfacc

github ↗package ↗homepage ↗

INSTALL

IMPORT

SIG · NERFACC

nerfacc

ai-mlpythonv0.5.3

harness data pending

Install & Compatibility

Where this runs

No compatibility data collected yet for this library.

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

OccGridEstimator

✓ from nerfacc import OccGridEstimator

✗ from nerfacc.estimators import OccGridEstimator

Old import path changed in v0.5.0

ContractionType

✓ from nerfacc import ContractionType

✗ from nerfacc.grid import ContractionType

ContractionType moved to top-level in v0.5.0

Initialize a multi-level occupancy grid and perform ray marching on a dummy grid.

import torch from nerfacc import OccGridEstimator device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') # Initialize an occupancy grid estimator for an unbounded scene (e.g., nerfstudio style) estimator = OccGridEstimator( roi_aabb=[-2.0, -2.0, -2.0, 2.0, 2.0, 2.0], resolution=256, levels=2, ).to(device) # Dummy occupancy update: random binary grid random_occ = torch.randint(0, 2, (estimator.binaries.shape[0], *estimator.binaries.shape[2:]), device=device, dtype=torch.bool) estimator.binaries = random_occ # Ray marching: generate rays and compute step sizes rays_o = torch.tensor([[0.0, 0.0, 0.0]], device=device) rays_d = torch.tensor([[1.0, 0.0, 0.0]], device=device) ray_indices, starts, ends, hits = estimator.marching(rays_o, rays_d, near_plane=0.0, far_plane=10.0) print(f"Number of ray steps: {starts.shape[0]}")

Debug

Known issues

breakingv0.5.0 rewrote 90% of the codebase: ContractionType removed from grid module, OccGridEstimator API changed (multi-level). Old code using single-level grid or contraction must be updated.

fix

Use OccGridEstimator with levels parameter; replace ContractionType imports from nerfacc.grid with nerfacc.ContractionType.

affects: <0.5.0

breakingContraction for Occupancy Grid is no longer supported in v0.5.0 due to inefficiency for ray traversal. Attempting to use contraction with OccGridEstimator will raise error.

fix

Remove contraction argument; for unbounded scenes use multi-level grid or ProposalNetworkEstimator instead.

affects: >=0.5.0

gotchaCUDA kernels are JIT compiled on first import; missing CUDA toolkit or incompatible PyTorch version will cause silent fallback to CPU or crash.

fix

Install compatible PyTorch + CUDA toolkit. Ensure torch.cuda.is_available() returns True.

affects: all

gotchaOccGridEstimator.binaries expects a boolean tensor of shape (levels, 1, res_x, res_y, res_z) after v0.5.0; single-level shape changed.

fix

Use estimator.binaries = occ.unsqueeze(0).unsqueeze(0) for single-level or initialize with levels=1.

affects: >=0.5.0

Errors

Common errors & fixes

ModuleNotFoundError: No module named 'nerfacc.estimators'

Import path changed in v0.5.0; estimators are now top-level.

fix

Use 'from nerfacc import OccGridEstimator' instead of 'from nerfacc.estimators import OccGridEstimator'.

AttributeError: module 'nerfacc' has no attribute 'ContractionType'

ContractionType was moved to nerfacc.grid; but in v0.5.0 it's at top-level.

fix

Use 'from nerfacc import ContractionType' (v0.5.0+) or 'from nerfacc.grid import ContractionType' (older versions).

RuntimeError: Expected all tensors to be on the same device...

Mixing CPU and CUDA tensors in estimator methods; estimator and rays must be on same device.

fix

Move estimator and rays to same device: estimator.to(device); rays_o = rays_o.to(device).

Upgrade

Version history

0.5.3latest on PyPI · released May 31, 2023

Audit

Dependencies

torchrequiredRequired for all operations; nerfacc builds CUDA kernels compatible with PyTorch

Agent activity

10 hits · last 30 days

node

Resources

githubhttps://github.com/KAIR-BAIR/nerfacc ↗

packagehttps://pypi.org/project/nerfacc/ ↗

homepagehttps://www.nerfacc.com ↗