IS MVSNet

IS-MVSNet (ECCV 2022)

紙|影片 |重量

我們的論文已被ECCV 2022接收為會議論文！

ISMVSNet，又稱為基於重要性取樣的 MVSNet，是一種簡單且有效的多視圖重建方法。

該儲存庫提供了基於 Mindspore 的 IS-MVSNet 實作。您可以加註星標並觀看此存儲庫以獲取進一步更新。

安裝

 # Centos 7.9.2009 is recommended.
# CUDA == 11.1, GCC == 7.3.0, Python == 3.7.9
conda create -n ismvsnet python=3.7.9
conda install mindspore-gpu=1.7.0 cudatoolkit=11.1 -c mindspore -c conda-forge  # Install mindspore == 1.7.0
pip install numpy, opencv-python, tqdm, Pillow
conda activate ismvsnet

預訓練模型

主幹網的預訓練權重已放置在./weights下方。第 1 至 3 階段的權重可以從預訓練權重下載。

資料結構

 DATAROOT
└───data
|   └───tankandtemples
|       └───intermediate
|           └───Playground
|           │   └───rmvs_scan_cams
|           │       │   00000000_cam.txt
|           │       │   00000001_cam.txt
|           │       │   ...
|           │   └───images
|           │       │   00000000.jpg
|           │       │   00000001.jpg
|           │       │   ...
|           │   └───pair.txt
|           │   └───Playground.log
|           └───Family
|           └───...
|       └───advanced
└───weights
└───src
└───validate.py
└───point_cloud_generator.py

深度預測

python validate.py

深度預測將保存到“results/{dataset_name}/{split}/深度”

點雲融合

python point_cloud_generator.py

融合的點雲將會儲存到“results/{dataset_name}/{split}/points”

引文

如果您認為此存儲庫有幫助，請考慮引用我們的論文：

 @InProceedings{ismvsnet,
author="Wang, Likang
and Gong, Yue
and Ma, Xinjun
and Wang, Qirui
and Zhou, Kaixuan
and Chen, Lei",
editor="Avidan, Shai
and Brostow, Gabriel
and Ciss{'e}, Moustapha
and Farinella, Giovanni Maria
and Hassner, Tal",
title="IS-MVSNet:Importance Sampling-Based MVSNet",
booktitle="Computer Vision -- ECCV 2022",
year="2022",
publisher="Springer Nature Switzerland",
address="Cham",
pages="668--683",
abstract="This paper presents a novel coarse-to-fine multi-view stereo (MVS) algorithm called importance-sampling-based MVSNet (IS-MVSNet) to address a crucial problem of limited depth resolution adopted by current learning-based MVS methods. We proposed an importance-sampling module for sampling candidate depth, effectively achieving higher depth resolution and yielding better point-cloud results while introducing no additional cost. Furthermore, we proposed an unsupervised error distribution estimation method for adjusting the density variation of the importance-sampling module. Notably, the proposed sampling module does not require any additional training and works reasonably well with the pre-trained weights of the baseline model. Our proposed method leads to up to {$}{$}20{backslash}times {$}{$}20{texttimes}promotion on the most refined depth resolution, thus significantly benefiting most scenarios and excellently superior on fine details. As a result, IS-MVSNet outperforms all the published papers on TNT's intermediate benchmark with an F-score of 62.82{%}. Code is available at github.com/NoOneUST/IS-MVSNet.",
isbn="978-3-031-19824-3"
}