Marrying Multi-view Geometry with Deep Priors for Image-based 3D Reconstruction

We live in a world where all interactions with the environment necessitate a 3D understanding of our surroundings. While humans excel at reasoning about 3D structures from both multi-view and single-view images, replicating this capability in computers remains challenging due to the need to combine mathematically proven geometric knowledge with end-to-end learned priors.

In this talk, the speaker introduces his work on narrowing this technical gap by devising new geometry-informed neural networks. These networks enhance the synergy of geometric principles and learned priors by: (1) combining single-view 3D reasoning with multi-view supervision; (2) embedding geometric aggregation into deep networks for multi-view stereo; and (3) fusing single and multi-view geometric cues for robust dynamic scene reconstruction. Finally, the speaker discusses new trends in geometry-informed networks that promise to advance this field.

Short bio:

Rui Li is a Ph.D. student at Northwestern Polytechnical University and a Visiting Researcher at King Abdullah University of Science and Technology (KAUST). He was a Visiting Student at the Computer Vision Lab of ETH Zürich under the supervision of Dr. Federico Tombari and Prof. Luc Van Gool.

Quan: 25/09/2024

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