diff --git a/3d-reconstruction/hashnerf/readme.md b/3d-reconstruction/hashnerf/readme.md
index bb0287e8daaf0b3431013bc732f7597940738b76..f8f41291b1506baec68f92a83ce7f8d004047a70 100644
--- a/3d-reconstruction/hashnerf/readme.md
+++ b/3d-reconstruction/hashnerf/readme.md
@@ -1,69 +1,65 @@
 # torch-ngp
-
 ## Model description
-
 A pytorch implementation of the NeRF part (grid encoder, density grid ray sampler) in instant-ngp, as described in Instant Neural Graphics Primitives with a Multiresolution Hash Encoding.
 
-## Step 1: Installing with pip
-
+## Step 1: Installing
 ```bash
-pip install -r requirements.txt
+pip3 install -r requirements.txt
 ```
 
-## Step 2: Training
+## Step 2: Prepare dataset
 
-We use the same data format as instant-ngp, e.g., [armadillo](https://github.com/NVlabs/instant-ngp/blob/master/data/sdf/armadillo.obj) and [fox](https://github.com/NVlabs/instant-ngp/tree/master/data/nerf/fox). 
-Please download and put them under `./data`.
+We use the same data format as instant-ngp, [fox](https://github.com/NVlabs/instant-ngp/tree/master/data/nerf/fox) and blender dataset [nerf_synthetic](https://drive.google.com/drive/folders/128yBriW1IG_3NJ5Rp7APSTZsJqdJdfc1).Please download and put them under `./data`.
 
-#bash
-bash train.sh
-
-We also support self-captured dataset and converting other formats (e.g., LLFF, Tanks&Temples, Mip-NeRF 360) to the nerf-compatible format, with details in the following code block.
-
-Supported datasets
+For custom dataset, you should:
+1. take a video / many photos from different views 
+2. put the video under a path like ./data/custom/video.mp4 or the images under 
+./data/custom/images/*.jpg.
+3. call the preprocess code: (should install ffmpeg and colmap first! refer to the file for more options)
+```bash
+python scripts/colmap2nerf.py --video ./data/custom/video.mp4 --run_colmap # if use video
+python scripts/colmap2nerf.py --images ./data/custom/images/ --run_colmap # if use images
+```
 
-  * [nerf_synthetic](https://drive.google.com/drive/folders/128yBriW1IG_3NJ5Rp7APSTZsJqdJdfc1) 
+## Step 3: Training and test
 
 ### One single GPU
 
 First time running will take some time to compile the CUDA extensions.
 
 ```bash
-### Instant-ngp NeRF
-# train with different backbones (with slower pytorch ray marching)
-# for the colmap dataset, the default dataset setting `--bound 2 --scale 0.33` is used.
-python main_nerf.py data/fox --workspace trial_nerf # fp32 mode
-python main_nerf.py data/fox --workspace trial_nerf --fp16 # fp16 mode (pytorch amp)
-
-
-# one for all: -O means --fp16 --cuda_ray --preload, which usually gives the best results balanced on speed & performance.
+# train with fox dataset
 python main_nerf.py data/fox --workspace trial_nerf -O
-```
+# data/fox is dataset path; --workspace means output path; -O means --fp16 --cuda_ray --preload, which usually gives the best results balanced on speed & performance.
 
-```bash
 # test mode
 python main_nerf.py data/fox --workspace trial_nerf -O --test
 ```
 
 ```bash
-# for the blender dataset, you should add `--bound 1.0 --scale 0.8 --dt_gamma 0`
+# train with the blender dataset, you should add `--bound 1.0 --scale 0.8 --dt_gamma 0`
 # --bound means the scene is assumed to be inside box[-bound, bound]
 # --scale adjusts the camera locaction to make sure it falls inside the above bounding box. 
 # --dt_gamma controls the adaptive ray marching speed, set to 0 turns it off.
 python main_nerf.py data/nerf_synthetic/lego --workspace trial_nerf -O --bound 1.0 --scale 0.8 --dt_gamma 0
-
-# then you can train as a colmap dataset (you'll need to tune the scale & bound if necessary):
-python main_nerf.py data/nerf_llff_data/fern --workspace trial_nerf -O
 ```
 
+```bash
+# train with custom dataset(you'll need to tune the scale & bound if necessary):
+python main_nerf.py data/custom_data --workspace trial_nerf -O
+```
 
 ## Results on BI-V100
 
+@@ -65,90 +60,4 @@ python main_nerf.py data/nerf_synthetic/lego --workspace trial_nerf -O
+
+
 | Convergence criteria | Configuration (x denotes number of GPUs) | Performance | Accuracy | Power（W） | Scalability | Memory utilization（G） | Stability |
 |----------------------|------------------------------------------|-------------|----------|------------|-------------|-------------------------|-----------|
 | 0.0652               | SDK V2.2,bs:1,1x,fp16                    | 10          | 11.9     | 82         | 0.903       | 28.1                    | 1         |
 
 
+
 ## Reference
 
 **Q**: How to choose the network backbone? 
@@ -81,74 +77,6 @@ python main_nerf.py data/nerf_llff_data/fern --workspace trial_nerf -O
 **Q**: Noisy novel views for realistic datasets.
 
 **A**: You could try setting `bg_radius` to a large value, e.g., 32. It trains an extra environment map to model the background in realistic photos. A larger `bound` will also help.
-An example for `bg_radius` in the [firekeeper](https://drive.google.com/file/d/19C0K6_crJ5A9ftHijUmJysxmY-G4DMzq/view?usp=sharing) dataset:
-![bg_model](./assets/bg_model.jpg)
-
 
-## Difference from the original implementation
-
-* Instead of assuming the scene is bounded in the unit box `[0, 1]` and centered at `(0.5, 0.5, 0.5)`, this repo assumes **the scene is bounded in box `[-bound, bound]`, and centered at `(0, 0, 0)`**. Therefore, the functionality of `aabb_scale` is replaced by `bound` here.
-* For the hashgrid encoder, this repo only implements the linear interpolation mode.
-* For TensoRF, we don't implement regularizations other than L1, and use `trunc_exp` as the density activation instead of `softplus`. The alpha mask pruning is replaced by the density grid sampler from instant-ngp, which shares the same logic for acceleration.
-
-
-## Citation
-
-If you find this work useful, a citation will be appreciated via:
-```
-@misc{torch-ngp,
-    Author = {Jiaxiang Tang},
-    Year = {2022},
-    Note = {https://github.com/ashawkey/torch-ngp},
-    Title = {Torch-ngp: a PyTorch implementation of instant-ngp}
-}
-
-@article{tang2022compressible,
-    title = {Compressible-composable NeRF via Rank-residual Decomposition},
-    author = {Tang, Jiaxiang and Chen, Xiaokang and Wang, Jingbo and Zeng, Gang},
-    journal = {arXiv preprint arXiv:2205.14870},
-    year = {2022}
-}
-```
 
-## Acknowledgement
-
-* Credits to [Thomas Müller](https://tom94.net/) for the amazing [tiny-cuda-nn](https://github.com/NVlabs/tiny-cuda-nn) and [instant-ngp](https://github.com/NVlabs/instant-ngp):
-    ```
-    @misc{tiny-cuda-nn,
-        Author = {Thomas M\"uller},
-        Year = {2021},
-        Note = {https://github.com/nvlabs/tiny-cuda-nn},
-        Title = {Tiny {CUDA} Neural Network Framework}
-    }
-
-    @article{mueller2022instant,
-        title = {Instant Neural Graphics Primitives with a Multiresolution Hash Encoding},
-        author = {Thomas M\"uller and Alex Evans and Christoph Schied and Alexander Keller},
-        journal = {arXiv:2201.05989},
-        year = {2022},
-        month = jan
-    }
-    ```
-
-* The framework of NeRF is adapted from [nerf_pl](https://github.com/kwea123/nerf_pl):
-    ```
-    @misc{queianchen_nerf,
-        author = {Quei-An, Chen},
-        title = {Nerf_pl: a pytorch-lightning implementation of NeRF},
-        url = {https://github.com/kwea123/nerf_pl/},
-        year = {2020},
-    }
-    ```
-
-* The official TensoRF [implementation](https://github.com/apchenstu/TensoRF):
-    ```
-    @article{TensoRF,
-      title={TensoRF: Tensorial Radiance Fields},
-      author={Chen, Anpei and Xu, Zexiang and Geiger, Andreas and Yu, Jingyi and Su, Hao},
-      journal={arXiv preprint arXiv:2203.09517},
-      year={2022}
-    }
-    ```
-
-* The NeRF GUI is developed with [DearPyGui](https://github.com/hoffstadt/DearPyGui).
+More information ref:ttps://github.com/ashawkey/torch-ngp
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