SurfEmb: Dense and Continuous Correspondence Distributions for Object Pose Estimation with Learnt Surface Embeddings

• URL: http://arxiv.org/abs/2111.13489v1
• Date: Fri, 26 Nov 2021 13:39:38 GMT
• Title: SurfEmb: Dense and Continuous Correspondence Distributions for Object Pose Estimation with Learnt Surface Embeddings
• Authors: Rasmus Laurvig Haugaard, Anders Glent Buch
• Abstract summary: We present an approach to learn dense, continuous 2D-3D correspondence distributions over the surface of objects from data. We also present a new method for 6D pose estimation of rigid objects using the learnt distributions to sample, score and refine pose hypotheses.
• Score: 2.534402217750793
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present an approach to learn dense, continuous 2D-3D correspondence distributions over the surface of objects from data with no prior knowledge of visual ambiguities like symmetry. We also present a new method for 6D pose estimation of rigid objects using the learnt distributions to sample, score and refine pose hypotheses. The correspondence distributions are learnt with a contrastive loss, represented in object-specific latent spaces by an encoder-decoder query model and a small fully connected key model. Our method is unsupervised with respect to visual ambiguities, yet we show that the query- and key models learn to represent accurate multi-modal surface distributions. Our pose estimation method improves the state-of-the-art significantly on the comprehensive BOP Challenge, trained purely on synthetic data, even compared with methods trained on real data. The project site is at https://surfemb.github.io/ .

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