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

作者：Mo Shan, Qiaojun Feng, You-Yi Jau, Nikolay Atanasov · 发表于：2021 IEEE/CVF International Conference on Computer Vision (ICCV) · 年份：2021 · DOI：10.1109/iccv48922.2021.00589 · 被引用次数：11 · 研究领域：Robotics and Sensor-Based Localization、3D Surveying and Cultural Heritage、3D Shape Modeling and Analysis

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.