FroDO: From Detections to 3D Objects

• URL: http://arxiv.org/abs/2005.05125v1
• Date: Mon, 11 May 2020 14:08:29 GMT
• Title: FroDO: From Detections to 3D Objects
• Authors: Kejie Li, Martin R\"unz, Meng Tang, Lingni Ma, Chen Kong, Tanner Schmidt, Ian Reid, Lourdes Agapito, Julian Straub, Steven Lovegrove, Richard Newcombe
• Abstract summary: FroDO is a method for accurate 3D reconstruction of object instances from RGB video. It infers object location, pose and shape in a coarse-to-fine manner. We evaluate on real-world datasets, including Pix3D, Redwood-OS, and ScanNet.
• Score: 29.10716046157072
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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