NodeSLAM: Neural Object Descriptors for Multi-View Shape Reconstruction

• URL: http://arxiv.org/abs/2004.04485v2
• Date: Sat, 10 Oct 2020 16:41:59 GMT
• Title: NodeSLAM: Neural Object Descriptors for Multi-View Shape Reconstruction
• Authors: Edgar Sucar, Kentaro Wada, and Andrew Davison
• Abstract summary: We present efficient and optimisable multi-class learned object descriptors together with a novel probabilistic and differential rendering engine. Our framework allows for accurate and robust 3D object reconstruction which enables multiple applications including robot grasping and placing, augmented reality, and the first object-level SLAM system.
• Score: 4.989480853499916
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The choice of scene representation is crucial in both the shape inference algorithms it requires and the smart applications it enables. We present efficient and optimisable multi-class learned object descriptors together with a novel probabilistic and differential rendering engine, for principled full object shape inference from one or more RGB-D images. Our framework allows for accurate and robust 3D object reconstruction which enables multiple applications including robot grasping and placing, augmented reality, and the first object-level SLAM system capable of optimising object poses and shapes jointly with camera trajectory.

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