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LCD: learned cross-domain descriptors for 2D-3D matching

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conference contribution
posted on 2020-01-01, 00:00 authored by Quang-Hieu Pham, Mikaela Angelina Uy, Binh-Son Hua, Duc Thanh NguyenDuc Thanh Nguyen, Gemma Roig, Sai-Kit Yeung
In this work, we present a novel method to learn a local cross-domain descriptor for 2D image and 3D point cloud matching. Our proposed method is a dual auto-encoder neural network that maps 2D and 3D input into a shared latent space representation. We show that such local cross-domain descriptors in the shared embedding are more discriminative than those obtained from individual training in 2D and 3D domains. To facilitate the training process, we built a new dataset by collecting ≈ 1.4 millions of 2D-3D correspondences with various lighting conditions and settings from publicly available RGB-D scenes. Our descriptor is evaluated in three main experiments: 2D-3D matching, cross-domain retrieval, and sparse-to-dense depth estimation. Experimental results confirm the robustness of our approach as well as its competitive performance not only in solving cross-domain tasks but also in being able to generalize to solve sole 2D and 3D tasks. Our dataset and code are released publicly at https://hkust-vgd.github.io/lcd.

History

Event

Association for the Advancement of Artificial Intelligence. Conference (34th : 2020 : New York, N.Y.)

Series

Association for the Advancement of Artificial Intelligence Conference

Pagination

11856 - 11864

Publisher

Association for the Advancement of Artificial Intelligence

Location

New York, N.Y.

Place of publication

Palo Alto, Calif.

Start date

2020-02-07

End date

2020-02-12

eISSN

2374-3468

ISBN-13

978-1-57735-835-0

Language

eng

Publication classification

E1 Full written paper - refereed

Editor/Contributor(s)

[Unknown]

Title of proceedings

AAAI-20 : Proceedings of the Thirty-fourth AAAI Conference on Artificial Intelligence

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