DELTA: Dense Depth from Events and LiDAR using Transformer's Attention
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866910928198434816 |
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| author | Brebion, Vincent Moreau, Julien Davoine, Franck |
| author_facet | Brebion, Vincent Moreau, Julien Davoine, Franck |
| contents | Event cameras and LiDARs provide complementary yet distinct data: respectively, asynchronous detections of changes in lighting versus sparse but accurate depth information at a fixed rate. To this day, few works have explored the combination of these two modalities. In this article, we propose a novel neural-network-based method for fusing event and LiDAR data in order to estimate dense depth maps. Our architecture, DELTA, exploits the concepts of self- and cross-attention to model the spatial and temporal relations within and between the event and LiDAR data. Following a thorough evaluation, we demonstrate that DELTA sets a new state of the art in the event-based depth estimation problem, and that it is able to reduce the errors up to four times for close ranges compared to the previous SOTA. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_02593 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DELTA: Dense Depth from Events and LiDAR using Transformer's Attention Brebion, Vincent Moreau, Julien Davoine, Franck Computer Vision and Pattern Recognition I.4.8 Event cameras and LiDARs provide complementary yet distinct data: respectively, asynchronous detections of changes in lighting versus sparse but accurate depth information at a fixed rate. To this day, few works have explored the combination of these two modalities. In this article, we propose a novel neural-network-based method for fusing event and LiDAR data in order to estimate dense depth maps. Our architecture, DELTA, exploits the concepts of self- and cross-attention to model the spatial and temporal relations within and between the event and LiDAR data. Following a thorough evaluation, we demonstrate that DELTA sets a new state of the art in the event-based depth estimation problem, and that it is able to reduce the errors up to four times for close ranges compared to the previous SOTA. |
| title | DELTA: Dense Depth from Events and LiDAR using Transformer's Attention |
| topic | Computer Vision and Pattern Recognition I.4.8 |
| url | https://arxiv.org/abs/2505.02593 |