FroDO: From Detections to 3D Objects

Kejie Li; Martin Rünz; Meng Tang; Lingni Ma; Chen Kong; Tanner Schmidt; Ian Reid; Lourdes Agapito; Julian Straub; Steven Lovegrove; Richard Newcombe

arXiv · 2005.05125

FroDO: From Detections to 3D Objects

Kejie Li·Martin Rünz·Meng Tang·Lingni Ma·Chen Kong·Tanner Schmidt·Ian Reid·Lourdes Agapito·Julian Straub·Steven Lovegrove·Richard Newcombe

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Abstract

Object-oriented maps are important for scene understanding since they jointly capture geometry and semantics, allow individual instantiation and meaningful reasoning about objects. We introduce FroDO, a method for accurate 3D reconstruction of object instances from RGB video that infers object location, pose and shape in a coarse-to-fine manner. Key to FroDO is to embed object shapes in a novel learnt space that allows seamless switching between sparse point cloud and dense DeepSDF decoding. Given an input sequence of localized RGB frames, FroDO first aggregates 2D detections to instantiate a category-aware 3D bounding box per object. A shape code is regressed using an encoder network before optimizing shape and pose further under the learnt shape priors using sparse and dense shape representations. The optimization uses multi-view geometric, photometric and silhouette losses. We evaluate on real-world datasets, including Pix3D, Redwood-OS, and ScanNet, for single-view, multi-view, and multi-object reconstruction.

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Kejie Li, Martin Rünz, Meng Tang, Lingni Ma, Chen Kong, Tanner Schmidt, Ian Reid, Lourdes Agapito, Julian Straub, Steven Lovegrove, Richard Newcombe. 2020-05-11. FroDO: From Detections to 3D Objects. https://arxiv.org/abs/2005.05125

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