An Overview of Recent Progress in Volumetric Semantic 3D Reconstruction

被引:0
|
作者
Hane, Christian [1 ]
Pollefeys, Marc [2 ,3 ]
机构
[1] Univ Calif Berkeley, Dept Elect Engn & Comp Sci, Berkeley, CA 94720 USA
[2] Swiss Fed Inst Technol, Dept Comp Sci, Zurich, Switzerland
[3] Microsoft, Redmond, WA USA
来源
2016 23RD INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR) | 2016年
基金
瑞士国家科学基金会;
关键词
OPTIMIZATION; SEGMENTATION; ALGORITHMS;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper gives an overview of a recently proposed method of solving dense 3D reconstruction and semantic segmentation from multiple input images in a joint fashion, i.e. as semantic 3D reconstruction. The formulation is cast as a volumetric fusion of depth maps and pixel-wise semantic classification scores. By posing the two problems as a joint optimization problem, both of the tasks can benefit from the other task's information. This leads to formulations which can reconstruct hidden unobserved surfaces. We give an overview of several papers which describe different ways of modeling the data term from the input data and also works which introduce object shape priors to the formulation. We present the basic convex multi-label formulation on which the method builds and also discuss the relation to other reconstruction algorithms which extract semantically annotated 3D models from images.
引用
收藏
页码:3294 / 3307
页数:14
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