ELLIPSDF: Joint Object Pose and Shape Optimization with a Bi-level Ellipsoid and Signed Distance Function Description

• URL: http://arxiv.org/abs/2108.00355v1
• Date: Sun, 1 Aug 2021 03:07:31 GMT
• Title: ELLIPSDF: Joint Object Pose and Shape Optimization with a Bi-level Ellipsoid and Signed Distance Function Description
• Authors: Mo Shan, Qiaojun Feng, You-Yi Jau, Nikolay Atanasov
• Abstract summary: This paper proposes an expressive yet compact model for joint object pose and shape optimization. It infers an object-level map from multi-view RGB-D camera observations. Our approach is evaluated on the large-scale real-world ScanNet dataset and compared against state-of-the-art methods.
• Score: 9.734266860544663
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Autonomous systems need to understand the semantics and geometry of their surroundings in order to comprehend and safely execute object-level task specifications. This paper proposes an expressive yet compact model for joint object pose and shape optimization, and an associated optimization algorithm to infer an object-level map from multi-view RGB-D camera observations. The model is expressive because it captures the identities, positions, orientations, and shapes of objects in the environment. It is compact because it relies on a low-dimensional latent representation of implicit object shape, allowing onboard storage of large multi-category object maps. Different from other works that rely on a single object representation format, our approach has a bi-level object model that captures both the coarse level scale as well as the fine level shape details. Our approach is evaluated on the large-scale real-world ScanNet dataset and compared against state-of-the-art methods.

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