A Global Depth-Range-Free Multi-View Stereo Transformer Network with Pose Embedding
This work addresses a key bottleneck in 3D reconstruction for computer vision applications by eliminating the need for depth range assumptions, though it is incremental in improving prior-free methods.
The paper tackles the problem of multi-view stereo reconstruction without requiring a depth range prior by proposing a framework that simultaneously processes all source images, achieving state-of-the-art results on the DTU dataset and Tanks&Temple benchmark.
In this paper, we propose a novel multi-view stereo (MVS) framework that gets rid of the depth range prior. Unlike recent prior-free MVS methods that work in a pair-wise manner, our method simultaneously considers all the source images. Specifically, we introduce a Multi-view Disparity Attention (MDA) module to aggregate long-range context information within and across multi-view images. Considering the asymmetry of the epipolar disparity flow, the key to our method lies in accurately modeling multi-view geometric constraints. We integrate pose embedding to encapsulate information such as multi-view camera poses, providing implicit geometric constraints for multi-view disparity feature fusion dominated by attention. Additionally, we construct corresponding hidden states for each source image due to significant differences in the observation quality of the same pixel in the reference frame across multiple source frames. We explicitly estimate the quality of the current pixel corresponding to sampled points on the epipolar line of the source image and dynamically update hidden states through the uncertainty estimation module. Extensive results on the DTU dataset and Tanks&Temple benchmark demonstrate the effectiveness of our method. The code is available at our project page: https://zju3dv.github.io/GD-PoseMVS/.