LLR-MVSNet: a lightweight network for low-texture scene reconstruction

Abstract

In recent years, learning-based MVS methods have achieved excellent performance compared with traditional methods. However, these methods still have notable shortcomings, such as the low efficiency of traditional convolutional networks and simple feature fusion, which lead to incomplete reconstruction. In this research, we propose a lightweight network for low-texture scene reconstruction (LLR-MVSNet). To improve accuracy and efficiency, a lightweight network is proposed, including a multi-scale feature extraction module and a weighted feature fusion module. The multi-scale feature extraction module uses depth-separable convolution and point-wise convolution to replace traditional convolution, which can reduce network parameters and improve the model efficiency. In order to improve the fusion accuracy, a weighted feature fusion module is proposed, which can selectively emphasize features, suppress useless information and improve the fusion accuracy. With rapid computational speed and high performance, our method surpasses the state-of-the-art benchmarks and performs well on the DTU and the Tanks & Temples datasets. The code of our method will be made available at https://github.com/wln19/LLR-MVSNet.