Associative3D: Volumetric Reconstruction from Sparse Views


Shengyi Qian*
Linyi Jin*
David F. Fouhey

University of Michigan

ECCV 2020

[pdf]
[code]
[1-min overview]
[video]


Given two views from unknown cameras, we aim to extract a coherent 3D space in terms of a set of volumetric objects placed in the scene. We represent the scene with a factored representation that splits the scene into per-object voxel grids with a scale and pose.

This paper studies the problem of 3D volumetric reconstruction from two views of a scene with an unknown camera. While seemingly easy for humans, this problem poses many challenges for computers since it requires simultaneously reconstructing objects in the two views while also figuring out their relationship. We propose a new approach that estimates reconstructions, distributions over the camera/object and camera/camera transformations, as well as an inter-view object affinity matrix. This information is then jointly reasoned over to produce the most likely explanation of the scene. We train and test our approach on a dataset of indoor scenes, and rigorously evaluate the merits of our joint reasoning approach. Our experiments show that it is able to recover reasonable scenes from sparse views, while the problem is still challenging.




Interactive Results

View A
View B
Associative3D
Ground Truth


Acknowledgements

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