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2026-03-02 14:10:02
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LatentFusion: End-to-End Differentiable Reconstruction and Rendering for Unseen Object Pose Estimation CVPR 2020 LatentFusion: End-to-End Differentiable Reconstruction and Rendering for Unseen Object Pose Estimation Keunhong Park , Arsalan Mousavian , Yu Xiang , Dieter Fox PDF arXiv Code Video Abstract Current 6D object pose estimation methods usually require a 3D model for each object. These methods also require additional training in order to incorporate new objects. As a result, they are difficult to scale to a large number of objects and cannot be directly applied to unseen objects. We propose a novel framework for 6D pose estimation of unseen objects. We present a network that reconstructs a latent 3D representation of an object using a small number of reference views at inference time. Our network is able to render the latent 3D represen...
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