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2D Semantic-Guided Semantic Scene Completion

delete2024-10-03
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PRE
AI
X
Xianzhu Liu
H
Haozhe Xie
S
Shengping Zhang *
H
Hongxun Yao
R
Rongrong Ji
Liqiang Nie 封面图
Liqiang Nie (Liqiang Nie)
D
Dacheng Tao
DOI:10.1007/s11263-024-02244-ydelete
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摘要

摘要

En 中文
Semantic scene completion (SSC) aims to simultaneously perform scene completion (SC) and predict semantic categories of a 3D scene from a single depth and/or RGB image. Most existing SSC methods struggle to handle complex regions with multiple objects close to each other, especially for objects with reflective or dark surfaces. This primarily stems from two challenges: (1) the loss of geometric information due to the unreliability of depth values from sensors, and (2) the potential for semantic confusion when simultaneously predicting 3D shapes and semantic labels. To address these problems, we propose a Semantic-guided Semantic Scene Completion framework, dubbed SG-SSC, which involves Semantic-guided Fusion (SGF) and Volume-guided Semantic Predictor (VGSP). Guided by 2D semantic segmentation maps, SGF adaptively fuses RGB and depth features to compensate for the missing geometric information caused by the missing values in depth images, thus performing more robustly to unreliable depth information. VGSP exploits the mutual benefit between SC and SSC tasks, making SSC more focused on predicting the categories of voxels with high occupancy probabilities and also allowing SC to utilize semantic priors to better predict voxel occupancy. Experimental results show that SG-SSC outperforms existing state-of-the-art methods on the NYU, NYUCAD, and SemanticKITTI datasets. Models and code are available at https://github.com/aipixel/SG-SSC.
Keyword:
Semantic scene completion
RGB-D images
Semantic-guided fusion
Volume-guided semantic predictor

期刊

International Journal of Computer Vision 封面图
International Journal of Computer Vision
IF:
9.3
论文数:
3.9K
被引数:
2.8W

机构

H
harbin institute of technology
学者数:
8.0W
论文数: 6.6W
被引数: 66
N
Nanyang Technological University
学者数:
4.9W
论文数: 4.8W
被引数: 8.1W
X
xiamen university
学者数:
5.9W
论文数: 3.8W
被引数: 67
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