From 47294e599f640d9a23150f3470f7b47a05e82fba Mon Sep 17 00:00:00 2001
From: Geralt_of_Rivia <87054407+guanjunwu@users.noreply.github.com>
Date: Fri, 12 Apr 2024 08:28:07 +0800
Subject: [PATCH] Update README.md

---
 README.md | 12 ++++++++----
 1 file changed, 8 insertions(+), 4 deletions(-)

diff --git a/README.md b/README.md
index 46b8725..531ee50 100644
--- a/README.md
+++ b/README.md
@@ -57,7 +57,9 @@ In our environment, we use pytorch=1.13.1+cu116.
 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.
+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
@@ -105,17 +107,19 @@ python scripts/downsample_point.py data/dynerf/cut_roasted_beef/colmap/dense/wor
 # Finally, train.
 python train.py -s data/dynerf/cut_roasted_beef --port 6017 --expname "dynerf/cut_roasted_beef" --configs arguments/dynerf/cut_roasted_beef.py 
 ```
-For training hypernerf scenes such as `virg/broom`, run
+For training hypernerf scenes such as `virg/broom`: Pregenerated point clouds by COLMAP are provided [here](https://drive.google.com/file/d/1fUHiSgimVjVQZ2OOzTFtz02E9EqCoWr5/view). Just download them and put them in to correspond folder, and you can skip the former two steps. Also, you can run the commands directly.
+
 ```python
 # First, computing dense point clouds by COLMAP
 bash colmap.sh data/hypernerf/virg/broom2 hypernerf
-# Second, downsample the point clouds generated in the first step.
+# Second, downsample the point clouds generated in the first step. 
 python scripts/downsample_point.py data/hypernerf/virg/broom2/colmap/dense/workspace/fused.ply data/hypernerf/virg/broom2/points3D_downsample2.ply
 # Finally, train.
 python train.py -s  data/hypernerf/virg/broom2/ --port 6017 --expname "hypernerf/broom2" --configs arguments/hypernerf/broom2.py 
 ```
 
-For your custom datasets, install nerfstudio and follow their colmap pipeline.
+
+For your custom datasets, install nerfstudio and follow their [COLMAP](https://colmap.github.io/) pipeline. You should install COLMAP at first, then:
 
 ```python
 pip install nerfstudio