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update_logs.md

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Update logs

  • 6.26: add support for D-NeRF.
  • 6.16: add support for CCNeRF.
  • 6.15: fixed a bug in raymarching, improved PSNR. Density thresh is directly applied on sigmas now (removed the empirical scaling factor).
  • 6.6: fix gridencoder to always use more accurate float32 inputs (coords), slightly improved performance (matched with tcnn).
  • 6.3: implement morton3D, misc improvements.
  • 5.29: fix a random bg color issue, add color_space option, better results for blender dataset.
  • 5.28: add a background model (set bg_radius > 0), which can suppress noises for real-world 360 datasets.
  • 5.21: expose more parameters to control, implement packbits.
  • 4.30: performance improvement (better lr_scheduler).
  • 4.25: add Tanks&Temples dataset support.
  • 4.18: add some experimental utils for random pose sampling and combined training with CLIP.
  • 4.13: add LLFF dataset support.
  • 4.13: also implmented tiled grid encoder according to this issue.
  • 4.12: optimized dataloader, add error_map sampling (experimental, will slow down training since will only sample hard rays...)
  • 4.10: add Windows support.
  • 4.9: use 6D AABB instead of a single bound for more flexible rendering. More options in GUI to control the AABB and dt_gamma for adaptive ray marching.
  • 4.9: implemented multi-res density grid (cascade) and adaptive ray marching. Now the fox renders much faster!
  • 4.6: fixed TensorCP hyper-parameters.
  • 4.3: add mark_untrained_grid to prevent training on out-of-camera regions. Add custom dataset instructions.
  • 3.31: better compatibility for lower pytorch versions.
  • 3.29: fix training speed for the fox dataset (balanced speed with performance...).
  • 3.27: major update. basically improve performance, and support tensoRF model.
  • 3.22: reverted from pre-generating rays as it takes too much CPU memory, still the PSNR for Lego can reach ~33 now.
  • 3.14: fixed the precision related issue for fp16 mode, and it renders much better quality. Added PSNR metric for NeRF.
  • 3.14: linearly scale desired_resolution with bound according to ashawkey#23.
  • 3.11: raymarching now supports supervising weights_sum (pixel alpha, or mask) directly, and bg_color is separated from CUDA to make it more flexible. Add an option to preload data into GPU.
  • 3.9: add fov for gui.
  • 3.1: add type='all' for blender dataset (load train + val + test data), which is the default behavior of instant-ngp.
  • 2.28: density_grid now stores density on the voxel center (with randomness), instead of on the grid. This should improve the rendering quality, such as the black strips in the lego scene.
  • 2.23: better support for the blender dataset.
  • 2.22: add GUI for NeRF training.
  • 2.21: add GUI for NeRF visualizing.
  • 2.20: cuda raymarching is finally stable now!
  • 2.15: add the official tinycudann as an alternative backend.
  • 2.10: add cuda_ray, can train/infer faster, but performance is worse currently.
  • 2.6: add support for RGBA image.
  • 1.30: fixed atomicAdd() to use __half2 in HashGrid Encoder's backward, now the training speed with fp16 is as expected!
  • 1.29: finished an experimental binding of fully-fused MLP. replace SHEncoder with a CUDA implementation.
  • 1.26: add fp16 support for HashGrid Encoder (requires CUDA >= 10 and GPU ARCH >= 70 for now...).