Related papers: Counting Stacked Objects from Multi-View Images

Counting Stacked Objects from Multi-View Images

URL: http://arxiv.org/abs/2411.19149v1
Date: Thu, 28 Nov 2024 13:51:16 GMT
Title: Counting Stacked Objects from Multi-View Images
Authors: Corentin Dumery, Noa Etté, Jingyi Xu, Aoxiang Fan, Ren Li, Hieu Le, Pascal Fua,
Abstract summary: We propose a novel 3D counting approach that decomposes the task into two complementary subproblems. By combining geometric reconstruction and deep learning-based depth analysis, our method can accurately count identical objects within containers. We validate our 3D Counting pipeline on diverse real-world and large-scale synthetic datasets.
Score: 57.68870743111393
License:
Abstract: Visual object counting is a fundamental computer vision task underpinning numerous real-world applications, from cell counting in biomedicine to traffic and wildlife monitoring. However, existing methods struggle to handle the challenge of stacked 3D objects in which most objects are hidden by those above them. To address this important yet underexplored problem, we propose a novel 3D counting approach that decomposes the task into two complementary subproblems - estimating the 3D geometry of the object stack and the occupancy ratio from multi-view images. By combining geometric reconstruction and deep learning-based depth analysis, our method can accurately count identical objects within containers, even when they are irregularly stacked. We validate our 3D Counting pipeline on diverse real-world and large-scale synthetic datasets, which we will release publicly to facilitate further research.

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