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DELTA: Dense Depth from Events and LiDAR Using Transformer's Attention

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Titel: DELTA: Dense Depth from Events and LiDAR Using Transformer's Attention
Autoren: Brebion, Vincent, Moreau, Julien, Davoine, Franck
Weitere Verfasser: Lund University, Faculty of Science, Centre for Environmental and Climate Science (CEC), Computational Science for Health and Environment, Lunds universitet, Naturvetenskapliga fakulteten, Centrum för miljö- och klimatvetenskap (CEC), Beräkningsvetenskap för hälsa och miljö, Originator, Lund University, Faculty of Science, Centre for Environmental and Climate Science (CEC), Lunds universitet, Naturvetenskapliga fakulteten, Centrum för miljö- och klimatvetenskap (CEC), Originator
Quelle: Proceedings - 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2025 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2025. :4898-4907
Schlagwörter: Natural Sciences, Computer and Information Sciences, Computer graphics and computer vision, Naturvetenskap, Data- och informationsvetenskap (Datateknik), Datorgrafik och datorseende
Beschreibung: 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.
Zugangs-URL: https://doi.org/10.1109/CVPRW67362.2025.00482
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  Data: DELTA: Dense Depth from Events and LiDAR Using Transformer's Attention
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  Data: 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.
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