今天读的是发表在CVPR2022上的无监督MVS文章，作者来自于国防科大。
文章链接：RayMVSNet
项目地址：Github

Abstract

作者希望直接优化每个camera ray上的深度值，所以提出这个RayMVSNet来学习1D implicit field的序列预测。使用了传统MVS里的方法进行极线搜索和transformer提取特征，并且使用了mutli-task learning。

1 Introduction

贡献主要是：

• 一个新颖的表现形式，来学习1D隐式场。
• epipolar transformer来学习特征。
• mutli-task learning来建模和预测，并且基于LSTM。
• 效果好。

2 Related Work

介绍了基于深度学习的MVS和implicit的表征。

3 Method

3.1 3D Cost Volume and Coarse Depth Prediction

Build a variance-based 3D cost volume and get coarse depth map.

3.2 Epipolar Transformer

Goal is to estimate the location of the zero-crossing point on each ray, so we can obtainthe depth map of reference image.

Why ray-based?

• depth map is view-dependent. So optimization is more straightforward and lightweight.
• all the 1D implicit fields share an identical spatial property, i.e. the monotonicity of the SDFs along the ray direction.

Zero-crossing hypothesis sampling

adopt coarse depth map and uniformly sample $K$ points P = { p k } 1 K P=\{p_k\}_{1}^{K} P={pk​}1K​ on the ray in the range of $\pm \delta$.

Epipolar transformer

Use 4 self-attention layers, each followed by 2 AddNorm and 1 feed-forward layer.

3.3 Ray-based 1D Implicit Field

Given the features of the hypothesized points, the ray-based 1D implicit fields are learned with an LSTM. Crucially, we leverage two attributes of LSTM.

• The mechanism of sequential processing inherently facilitates the learning of the SDF monotonicity along the ray direction.
• The property of time invariance increases the network robustness by allowing the zero-crossing position to appear at any place (time-step) on the ray.

3.4 Implementations

4 Results and Evaluation

5 Conclusion