3D Registration for Self-Occluded Objects in Context

• URL: http://arxiv.org/abs/2011.11260v1
• Date: Mon, 23 Nov 2020 08:05:28 GMT
• Title: 3D Registration for Self-Occluded Objects in Context
• Authors: Zheng Dang and Fei Wang and Mathieu Salzmann
• Abstract summary: We introduce the first deep learning framework capable of effectively handling this scenario. Our method consists of an instance segmentation module followed by a pose estimation one. It allows us to perform 3D registration in a one-shot manner, without requiring an expensive iterative procedure.
• Score: 66.41922513553367
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While much progress has been made on the task of 3D point cloud registration, there still exists no learning-based method able to estimate the 6D pose of an object observed by a 2.5D sensor in a scene. The challenges of this scenario include the fact that most measurements are outliers depicting the object's surrounding context, and the mismatch between the complete 3D object model and its self-occluded observations. We introduce the first deep learning framework capable of effectively handling this scenario. Our method consists of an instance segmentation module followed by a pose estimation one. It allows us to perform 3D registration in a one-shot manner, without requiring an expensive iterative procedure. We further develop an on-the-fly rendering-based training strategy that is both time- and memory-efficient. Our experiments evidence the superiority of our approach over the state-of-the-art traditional and learning-based 3D registration methods.

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