A preliminary study on view independent panoptic scene change detection
A preliminary study on view independent panoptic scene change detection
复制标题
视角无关的全景场景变化检测初步研究
DOI：
10.1117/12.2666872
复制
发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Murase Hiroshi
中科院分区：
文献类型：
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作者：
Li Jiaxin;Kawanishi Yasutomo;Deguchi Daisuke;Murase Hiroshi
探索室内环境和发现场景中出现的未知物体是机器人场景理解研究的重要内容。然而，背景减法传统上用于分割未知目标区域，不能直接用于机器人上的移动摄像机。在本文中，我们提出了一种视点无关的全景场景变化检测任务，即通过比较物体出现前后不同视点的两幅图像来分割未知物体区域。本文提出了一种将分割后的已知实例区域作为背景来分割未知目标区域的方法。在背景建模方面，我们介绍了基于直方图的方法和基于深度度量学习的方法。此外，我们创建了一个新的全景场景变化检测数据集，该数据集由从不同相机视图拍摄的图像组成。通过实验，我们证实了该方法可以分割未知类实例的区域；基于深度度量学习的方法比基于直方图的方法更准确，在变化检测数据集上取得了良好的性能。
Exploring the indoor environment and finding unknown objects that appeared in a scene are important for research of scene understanding by a robot. However, background subtraction is traditionally used for segmenting unknown object regions, and it cannot be directly used for a moving camera on the robot. In this paper, we propose a task called view-independent panoptic scene change detection, which is the task of segmenting unknown object regions by comparing two images from different viewpoints before and after the objects appear. In this paper, we propose a method for segmenting unknown object regions by modeling a segmented known instance region as background. For the background modeling, we introduce two methods: histogram-based and deep metric-learning-based methods. In addition, we create a new panoptic scene change detection dataset consisting of images taken from different camera views. Through experiments, we confirm that the proposed method can segment regions of unknown class instances; the deep metric-learning-based method performs more accurately than the histogram-based method, achieving good performance on the change detection dataset.