Merge pull request #87 from hustvl/latest

Latest

README.md

@@ -1,10 +1,9 @@
 # 4D Gaussian Splatting for Real-Time Dynamic Scene Rendering
 
-## ArXiv Preprint
+## CVPR 2024
 
 ### [Project Page](https://guanjunwu.github.io/4dgs/index.html)| [arXiv Paper](https://arxiv.org/abs/2310.08528)
 
-
 [Guanjun Wu](https://guanjunwu.github.io/) <sup>1*</sup>, [Taoran Yi](https://github.com/taoranyi) <sup>2*</sup>,
 [Jiemin Fang](https://jaminfong.cn/) <sup>3‡</sup>, [Lingxi Xie](http://lingxixie.com/) <sup>3 </sup>, </br>[Xiaopeng Zhang](https://scholar.google.com/citations?user=Ud6aBAcAAAAJ&hl=zh-CN) <sup>3 </sup>, [Wei Wei](https://www.eric-weiwei.com/) <sup>1 </sup>,[Wenyu Liu](http://eic.hust.edu.cn/professor/liuwenyu/) <sup>2 </sup>, [Qi Tian](https://www.qitian1987.com/) <sup>3 </sup> , [Xinggang Wang](https://xwcv.github.io) <sup>2‡✉</sup>
 
@@ -12,18 +11,27 @@
 
 <sup>\*</sup> Equal Contributions. <sup>$\ddagger$</sup> Project Lead. <sup>✉</sup> Corresponding Author.
 
----------------------------------------------------
+---
 
 ![block](assets/teaserfig.jpg)
 Our method converges very quickly and achieves real-time rendering speed.
 
-Colab demo:[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/hustvl/4DGaussians/blob/master/4DGaussians.ipynb) (Thanks [camenduru](https://github.com/camenduru/4DGaussians-colab).)
+New Colab demo:[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1wz0D5Y9egAlcxXy8YO9UmpQ9oH51R7OW?usp=sharing) (Thanks [Tasmay-Tibrewal
+](https://github.com/Tasmay-Tibrewal))
+
+Old Colab demo:[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/hustvl/4DGaussians/blob/master/4DGaussians.ipynb) (Thanks [camenduru](https://github.com/camenduru/4DGaussians-colab).)
 
 Light Gaussian implementation: [This link](https://github.com/pablodawson/4DGaussians) (Thanks [pablodawson](https://github.com/pablodawson))
 
 
+## News
+
+2024.02: Accepted by CVPR 2024. We delete some logging settings for debugging, the corrected training time is only **8 mins** (20 mins before) in D-NeRF datasets and **30 mins** (1 hour before) in HyperNeRF datasets. The rendering quality is not affected.
+
 ## Environmental Setups
+
 Please follow the [3D-GS](https://github.com/graphdeco-inria/gaussian-splatting) to install the relative packages.
+
 ```bash
 git clone https://github.com/hustvl/4DGaussians
 cd 4DGaussians
@@ -35,13 +43,17 @@ pip install -r requirements.txt
 pip install -e submodules/depth-diff-gaussian-rasterization
 pip install -e submodules/simple-knn
 ```
+
 In our environment, we use pytorch=1.13.1+cu116.
+
 ## Data Preparation
+
 **For synthetic scenes:**
 The dataset provided in [D-NeRF](https://github.com/albertpumarola/D-NeRF) is used. You can download the dataset from [dropbox](https://www.dropbox.com/s/0bf6fl0ye2vz3vr/data.zip?dl=0).
 
 **For real dynamic scenes:**
 The dataset provided in [HyperNeRF](https://github.com/google/hypernerf) is used. You can download scenes from [Hypernerf Dataset](https://github.com/google/hypernerf/releases/tag/v0.1) and organize them as [Nerfies](https://github.com/google/nerfies#datasets). Meanwhile, [Plenoptic Dataset](https://github.com/facebookresearch/Neural_3D_Video) could be downloaded from their official websites. To save the memory, you should extract the frames of each video and then organize your dataset as follows.
+
 ```
 ├── data
 │   | dnerf 
@@ -69,46 +81,56 @@ The dataset provided in [HyperNeRF](https://github.com/google/hypernerf) is used
 |     ├── ...
 ```
 
-
 ## Training
+
 For training synthetic scenes such as `bouncingballs`, run
+
 ```
 python train.py -s data/dnerf/bouncingballs --port 6017 --expname "dnerf/bouncingballs" --configs arguments/dnerf/bouncingballs.py 
 ```
+
 You can customize your training config through the config files.
 
-# Checkpoint
+Checkpoint
+
 Also, you can training your model with checkpoint.
+
 ```python
 python train.py -s data/dnerf/bouncingballs --port 6017 --expname "dnerf/bouncingballs" --configs arguments/dnerf/bouncingballs.py --checkpoint_iterations 200 # change it.
 ```
+
 Then load checkpoint with:
+
 ```python
 python train.py -s data/dnerf/bouncingballs --port 6017 --expname "dnerf/bouncingballs" --configs arguments/dnerf/bouncingballs.py --start_checkpoint "output/dnerf/bouncingballs/chkpnt_coarse_200.pth"
 # finestage: --start_checkpoint "output/dnerf/bouncingballs/chkpnt_fine_200.pth"
 ```
 
 ## Rendering
+
 Run the following script to render the images.
 
 ```
 python render.py --model_path "output/dnerf/bouncingballs/"  --skip_train --configs arguments/dnerf/bouncingballs.py  &
 ```
 
-
 ## Evaluation
+
 You can just run the following script to evaluate the model.
 
 ```
 python metrics.py --model_path "output/dnerf/bouncingballs/" 
 ```
+
 ## Custom Datasets
+
 Install nerfstudio and follow their colmap pipeline.
 
 ```
 pip install nerfstudio
 ns-process-data images --data data/your-data --output-dir data/your-ns-data
-python train.py -s data/your-ns-data --port 6017 --expname "custom" --configs arguments/hypernerf/default.py 
+cp -r data/your-ns-data/images data/your-ns-data/colmap/images
+python train.py -s data/your-ns-data/colmap --port 6017 --expname "custom" --configs arguments/hypernerf/default.py 
 
 ```
 
@@ -120,6 +142,7 @@ There are some helpful scripts in , please feel free to use them.
 get all points clouds at each timestamps.
 
 usage:
+
 ```python
 export exp_name="hypernerf"
 python vis_point.py --model_path output/$exp_name/interp/aleks-teapot --configs arguments/$exp_name/default.py 
@@ -132,6 +155,7 @@ visualize the weight of Multi-resolution HexPlane module.
 `merge_many_4dgs.py`:
 merge your trained 4dgs.
 usage:
+
 ```python
 export exp_name="dynerf"
 python merge_many_4dgs.py --model_path output/$exp_name/sear_steak
@@ -139,6 +163,7 @@ python merge_many_4dgs.py --model_path output/$exp_name/sear_steak
 
 `colmap.sh`:
 generate point clouds from input data
+
 ```bash
 bash colmap.sh data/hypernerf/virg/vrig-chicken hypernerf 
 bash colmap.sh data/dynerf/sear_steak llff
@@ -147,26 +172,50 @@ bash colmap.sh data/dynerf/sear_steak llff
 **Blender** format seems doesn't work. Welcome to raise a pull request to fix it.
 
 `downsample_point.py` :downsample generated point clouds by sfm.
+
 ```python
 python scripts/downsample_point.py data/dynerf/sear_steak/colmap/dense/workspace/fused.ply data/dynerf/sear_steak/points3D_downsample2.ply
 ```
+
 In my paper, I always use `colmap.sh` to generate dense point clouds and downsample it to less than 40000 points.
 
 Here are some codes maybe useful but never adopted in my paper, you can also try it.
 
+## Further works
+
+We sincerely thank the authors and their fantastic works for other applications based on our code.
+
+[MD-Splatting: Learning Metric Deformation from 4D Gaussians in Highly Deformable Scenes](https://md-splatting.github.io/)
+
+[4DGen: Grounded 4D Content Generation with Spatial-temporal Consistency](https://vita-group.github.io/4DGen/)
+
+[DreamGaussian4D: Generative 4D Gaussian Splatting](https://github.com/jiawei-ren/dreamgaussian4d)
+
+[EndoGaussian: Real-time Gaussian Splatting for Dynamic Endoscopic Scene Reconstruction](https://github.com/yifliu3/EndoGaussian)
+
+[EndoGS: Deformable Endoscopic Tissues Reconstruction with Gaussian Splatting](https://github.com/HKU-MedAI/EndoGS)
+
+[Endo-4DGS: Endoscopic Monocular Scene Reconstruction with 4D Gaussian Splatting](https://arxiv.org/abs/2401.16416)
+
+
 ---
+
 ## Contributions
 
 **This project is still under development. Please feel free to raise issues or submit pull requests to contribute to our codebase.**
 
 ---
+
 Some source code of ours is borrowed from [3DGS](https://github.com/graphdeco-inria/gaussian-splatting), [k-planes](https://github.com/Giodiro/kplanes_nerfstudio),[HexPlane](https://github.com/Caoang327/HexPlane), [TiNeuVox](https://github.com/hustvl/TiNeuVox). We sincerely appreciate the excellent works of these authors.
 
 ## Acknowledgement
 
 We would like to express our sincere gratitude to [@zhouzhenghong-gt](https://github.com/zhouzhenghong-gt/) for his revisions to our code and discussions on the content of our paper.
+
 ## Citation
+
 Some insights about neural voxel grids and dynamic scenes reconstruction originate from [TiNeuVox](https://github.com/hustvl/TiNeuVox). If you find this repository/work helpful in your research, welcome to cite these papers and give a ⭐.
+
 ```
 @article{wu20234dgaussians,
   title={4D Gaussian Splatting for Real-Time Dynamic Scene Rendering},

arguments/dnerf/dnerf_default.py

@@ -11,7 +11,7 @@ OptimizationParams = dict(
     iterations = 20000,
     pruning_interval = 8000,
     percent_dense = 0.01,
-    render_process=True,
+    render_process=False,
     # no_do=False,
     # no_dshs=False
     

arguments/hypernerf/default.py

@@ -11,7 +11,7 @@ ModelHiddenParams = dict(
     plane_tv_weight = 0.0002,
     time_smoothness_weight = 0.001,
     l1_time_planes =  0.0001,
-    render_process=True
+    render_process=False
 )
 OptimizationParams = dict(
     # dataloader=True,

submodules/depth-diff-gaussian-rasterization

@@ -1 +1 @@
-Subproject commit e49506654e8e11ed8a62d22bcb693e943fdecacf
+Subproject commit f2d8fa9921ea9a6cb9ac1c33a34ebd1b11510657

train.py

@@ -403,8 +403,8 @@ if __name__ == "__main__":
     parser.add_argument('--port', type=int, default=6009)
     parser.add_argument('--debug_from', type=int, default=-1)
     parser.add_argument('--detect_anomaly', action='store_true', default=False)
-    parser.add_argument("--test_iterations", nargs="+", type=int, default=[500*i for i in range(100)])
+    parser.add_argument("--test_iterations", nargs="+", type=int, default=[3000,14000,20000])
-    parser.add_argument("--save_iterations", nargs="+", type=int, default=[1000, 3000, 4000, 5000, 6000, 7_000, 9000, 10000, 12000, 14000, 20000, 30_000, 45000, 60000])
+    parser.add_argument("--save_iterations", nargs="+", type=int, default=[3000,14000,20000, 30_000, 45000, 60000])
     parser.add_argument("--quiet", action="store_true")
     parser.add_argument("--checkpoint_iterations", nargs="+", type=int, default=[])
     parser.add_argument("--start_checkpoint", type=str, default = None)