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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 representation from arbitrary views using this neural renderer we directly optimize for pose given an input image by training our network with a large number of 3d shapes for reconstruction and rendering our network generalizes well to unseen objects we present a new dataset for unseen object pose estimation moped we evaluate the performance of our method for unseen object pose estimation on moped as well as the modelnet and linemod datasets our method performs competitively to supervised methods that are trained on those objects citing latentfusion if you find the latentfusion code or data useful please consider citing inproceedings park2019latentfusion title latentfusion end to end differentiable reconstruction and rendering for unseen object pose estimation author park keunhong and mousavian arsalan and xiang yu and fox dieter booktitle proceedings of the ieee conference on computer vision and pattern recognition year 2020 code you can find our code on github moped dataset the dataset is available for download here dataset license copyright 2020 nvidia permission is hereby granted free of charge to any person obtaining a copy of this software and associated documentation files the software to deal in the software without restriction including without limitation the rights to use copy modify merge publish distribute sublicense and or sell copies of the software and to permit persons to whom the software is furnished to do so subject to the following conditions the above copyright notice and this permission notice shall be included in all copies or substantial portions of the software the software is provided as is without warranty of any kind express or implied including but not limited to the warranties of merchantability fitness for a particular purpose and noninfringement in no event shall the authors or copyright holders be liable for any claim damages or other liability whether in an action of contract tort or otherwise arising from out of or in connection with the software or the use or other dealings in the software