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

Mo Shan, Qiaojun Feng, You-Yi Jau, Nikolay Atanasov; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 5946-5955

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.