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Abstract
We introduce visual hints expansion for guiding stereo matching to improve generalization. Our work is motivated by the robustness of Visual Inertial Odometry (VIO) in computer vision and robotics, where a sparse and unevenly distributed set of feature points characterizes a scene. To improve stereo matching, we propose to elevate 2D hints to 3D points. These sparse and unevenly distributed 3D visual hints are expanded using a 3D random geometric graph, which enhances the learning and inference process. We evaluate our proposal on multiple widely adopted benchmarks and show improved performance without access to additional sensors other than the image sequence. To highlight practical applicability and symbiosis with visual odometry, we demonstrate how our methods run on embedded hardware.
Original language | English |
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Title of host publication | Proceedings - 2023 IEEE Winter Conference on Applications of Computer Vision, WACV 2023 |
Publisher | IEEE |
Pages | 5829-5838 |
Number of pages | 10 |
ISBN (Electronic) | 978-1-6654-9346-8 |
DOIs | |
Publication status | Published - 2023 |
MoE publication type | A4 Article in a conference publication |
Event | IEEE Winter Conference on Applications of Computer Vision - Waikoloa, United States Duration: 2 Jan 2023 → 7 Jan 2023 |
Conference
Conference | IEEE Winter Conference on Applications of Computer Vision |
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Abbreviated title | WACV |
Country/Territory | United States |
City | Waikoloa |
Period | 02/01/2023 → 07/01/2023 |
Keywords
- Algorithms: 3D computer vision
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Solin Arno /AoF Fellow Salary: Probabilistic principles for latent space exploration in deep learning
01/09/2021 → 31/08/2026
Project: Academy of Finland: Other research funding
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-: Finnish Center for Artificial Intelligence
01/01/2019 → 31/12/2022
Project: Academy of Finland: Other research funding