Change Log

April 5, 2023 ยท View on GitHub

All notable changes to this project will be documented in this file.

The format is based on Keep a Changelog and this project adheres to Semantic Versioning.

[0.5.0] - 2023-04-04

This is a major upgrade in which 90% of the code has been rewritten. In this version we achieves:

Teaser

Links:

Methodologies:

  • Upgrade Occupancy Grid to support multiple levels.
  • Support Proposal Network from Mip-NeRF 360.
  • Update examples on unbounded scenes to use Multi-level Occupancy Grid or Proposal Network.
  • Contraction for Occupancy Grid is no longer supported due to it's inefficiency for ray traversal.

API Changes:

  • [Changed] OccupancyGrid() -> OccGridEstimator().
    • [Added] Argument levels=1 for multi-level support.
    • [Added] Function self.sampling() that does basically the same thing with the old nerfacc.ray_marching.
    • [Renamed] Function self.every_n_step() -> self.update_every_n_steps()
  • [Added] PropNetEstimator(). With functions self.sampling(), self.update_every_n_steps() and self.compute_loss().
  • [Removed] ray_marching(). Ray marching is now implemented through calling sampling() of the OccGridEstimator() / PropNetEstimator().
  • [Changed] ray_aabb_intersect() now supports multiple aabb, and supports new argument near_plane, far_plane, miss_value.
  • [Changed] render_*_from_*(). The input shape changes from (all_samples, 1) to (all_samples). And the function will returns all intermediate results so it might be a tuple.
  • [Changed] rendering(). The input shape changes from (all_samples, 1) to (all_samples), including the shape assumption for the rgb_sigma_fn and rgb_alpha_fn. Be aware of this shape change.
  • [Changed] accumulate_along_rays(). The shape of the weights in the inputs should be (all_samples) now.
  • [Removed] unpack_info(), pack_data(), unpack_data() are temporally removed due to in-compatibility with the new backend implementation. Will add them back later.
  • [Added] Some basic functions that support both batched tensor and flattened tensor: inclusive_prod(), inclusive_sum(), exclusive_prod(), exclusive_sum(), importance_sampling(), searchsorted().

Examples & Benchmarks:

  • More benchmarks and examples. See folder examples/ and benchmarks/.

[0.3.5] - 2023-02-23

A stable version that achieves:

  • The vanilla Nerf model with 8-layer MLPs can be trained to better quality (+0.5 PNSR) in 1 hour rather than days as in the paper.
  • The Instant-NGP Nerf model can be trained to equal quality in 4.5 minutes, comparing to the official pure-CUDA implementation.
  • The D-Nerf model for dynamic objects can also be trained in 1 hour rather than 2 days as in the paper, and with better quality (+~2.5 PSNR).
  • Both bounded and unbounded scenes are supported.

Links:

Methodologies:

  • Single resolution nerfacc.OccupancyGrid for synthetic scenes.
  • Contraction methods nerfacc.ContractionType for unbounded scenes.