计算机科学
灰度
估计员
算法
人工智能
比例(比率)
编码(集合论)
水准点(测量)
一般化
卷积(计算机科学)
像素
计算机视觉
人工神经网络
数学
地理
程序设计语言
数学分析
集合(抽象数据类型)
物理
统计
量子力学
大地测量学
作者
Huizhou Zhou,Haoliang Zhao,Qi Wang,Ge‐Fei Hao,Liang Lei
标识
DOI:10.1016/j.neunet.2023.03.012
摘要
Multi-view stereo reconstruction aims to construct 3D scenes from multiple 2D images. In recent years, learning-based multi-view stereo methods have achieved significant results in depth estimation for multi-view stereo reconstruction. However, the current popular multi-stage processing method cannot solve the low-efficiency problem satisfactorily owing to the use of 3D convolution and still involves significant amounts of calculation. Therefore, to further balance the efficiency and generalization performance, this study proposed a multi-scale iterative probability estimation with refinement, which is a highly efficient method for multi-view stereo reconstruction. It comprises three main modules: 1) a high-precision probability estimator, dilated-LSTM that encodes the pixel probability distribution of depth in the hidden state, 2) an efficient interactive multi-scale update module that fully integrates multi-scale information and improves parallelism by interacting information between adjacent scales, and 3) a Pi-error Refinement module that converts the depth error between views into a grayscale error map and refines the edges of objects in the depth map. Simultaneously, we introduced a large amount of high-frequency information to ensure the accuracy of the refined edges. Among the most efficient methods (e.g., runtime and memory), the proposed method achieved the best generalization on the Tanks & Temples benchmarks. Additionally, the performance of the Miper-MVS was highly competitive in DTU benchmark. Our code is available at https://github.com/zhz120/Miper-MVS.
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