change of log (#185)

CHANGELOG.md

+
+# Change Log
+All notable changes to this project will be documented in this file.
+ 
+The format is based on [Keep a Changelog](http://keepachangelog.com/)
+and this project adheres to [Semantic Versioning](http://semver.org/).
+ 
+## [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](/docs/source/_static/images/teaser.jpg?raw=true)
+ 
+Links:
+- Documentation: https://www.nerfacc.com/en/v0.5.0/
+- ArXiv Report: Coming Soon.
+
+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 1~2 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:
+- Documentation: https://www.nerfacc.com/en/v0.3.5/
+- ArXiv Report: https://arxiv.org/abs/2210.04847v2/
+
+Methodologies:
+- Single resolution `nerfacc.OccupancyGrid` for synthetic scenes.
+- Contraction methods `nerfacc.ContractionType` for unbounded scenes.
\ No newline at end of file

nerfacc/grid.py

@@ -31,9 +31,11 @@ def ray_aabb_intersect(
             Default to infinity.
 
     Returns:
-        t_mins: (n_rays, m) tmin for each ray-AABB pair.
-        t_maxs: (n_rays, m) tmax for each ray-AABB pair.
-        hits: (n_rays, m) whether each ray-AABB pair intersects.
+        A tuple of {Tensor, Tensor, BoolTensor}:
+    
+        - **t_mins**: (n_rays, m) tmin for each ray-AABB pair.
+        - **t_maxs**: (n_rays, m) tmax for each ray-AABB pair.
+        - **hits**: (n_rays, m) whether each ray-AABB pair intersects.
     """
     assert rays_o.ndim == 2 and rays_o.shape[-1] == 3
     assert rays_d.ndim == 2 and rays_d.shape[-1] == 3