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VALSE 论文速览 第174期:PlaneRecTR: Query Learning for Plane Recovery

2024-5-21 13:35| 发布者: 程一-计算所| 查看: 341| 评论: 0

摘要: 为了使得视觉与学习领域相关从业者快速及时地了解领域的最新发展动态和前沿技术进展,VALSE最新推出了《论文速览》栏目,将在每周发布一至两篇顶会顶刊论文的录制视频,对单个前沿工作进行细致讲解。本期VALSE论文速 ...

为了使得视觉与学习领域相关从业者快速及时地了解领域的最新发展动态和前沿技术进展,VALSE最新推出了《论文速览》栏目,将在每周发布一至两篇顶会顶刊论文的录制视频,对单个前沿工作进行细致讲解。本期VALSE论文速览选取了来自国防科技大学的单目3D平面重建 (3D Plane Recovery from a Single View)的工作。该工作由该校徐凯老师和智帅峰老师指导,论文一作史静佳同学录制。


论文题目:

PlaneRecTR: Unified Query Learning for 3D Plane Recovery from a Single View

作者列表:

史静佳 (国防科技大学)、智帅峰 (国防科技大学)、徐凯 (国防科技大学)


B站观看网址:

https://www.bilibili.com/video/BV1im421n7KK/



论文摘要:

3D plane recovery from a single image can usually be divided into several subtasks of plane detection, segmentation, parameter estimation and possibly depth estimation. Previous works tend to solve it by either extending the RCNN-based segmentation network or the dense pixel embedding-based clustering framework. However, none of them tried to integrate above related subtasks into a unified framework but treated them separately and sequentially, which we suspect is potentially a main source of performance limitation for existing approaches. Motivated by this finding and the success of query-based learning in enriching reasoning among semantic entities, in this paper, we propose PlaneRecTR, a Transformer-based architecture, which for the first time unifies all subtasks related to single view plane recovery with a single compact  model. Extensive quantitative and qualitative experiments demonstrate that our proposed unified learning achieves mutual benefits across subtasks, obtaining a new state-of-the-art performance on public ScanNet and NYUv2-Plane datasets. 


参考文献:

[1] Jingjia Shi, Shuaifeng Zhi, and Kai Xu. "PlaneRecTR: Unified Query Learning for 3D Plane Recovery from a Single View." Proceedings of the IEEE/CVF International Conference on Computer Vision. 2023.


论文链接:

[https://openaccess.thecvf.com/content/ICCV2023/html/Shi_PlaneRecTR_Unified_Query_Learning_for_3D_Plane_Recovery_from_a_ICCV_2023_paper.html]

 

代码链接:

[https://github.com/SJingjia/PlaneRecTR]

 

视频讲者简介:

史静佳,国防科技大学计算机学院直博研究生,本科毕业于中南大学智能科学与技术专业,目前的研究方向为结构化场景重建。



特别鸣谢本次论文速览主要组织者:

月度轮值AC:胡迪 (中国人民大学)


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