LiDAR Challenges in Camera-Centric Perception
Abstract
LiDAR provides essential 3D geometry for perception, but its role in camera-centric systems remains challenging due to sparse point clouds, limited semantic detail, weather sensitivity, and the cost of cross-sensor fusion. These issues can make tasks such as semantic segmentation and dense alignment more difficult, even though LiDAR still offers strong advantages for depth estimation, object separation, and tracking stability. The LiDAR reward: a 0.26 Gbps point cloud data replaces 3 Gbps of camera data plus 0.5–60 TFLOPS of depth reconstruction, with error flat across range. This talk covers selective fusion, adaptive attention, and compute-efficient processing to cut latency and bandwidth.
- Published in
- Proceedings of the Raptors Conference, Vol. 1 (2026)
- Conference
- Raptors Conference 2026, 21 October 2026, online
- Accepted
- 2026-10-14
- DOI
- https://doi.org/10.68495/rc.2026.C049
- Presentation
- Slides are not yet available for this paper.
- Licence
- © 2026Anushree Nagvekar. Published under Creative Commons Attribution 4.0 International (CC BY 4.0).
- Publisher
- Hackathon Raptors, Community Interest Company 15557917, London, United Kingdom
- Permanent address
- https://proceedings.raptors.dev/nagvekar-lidar-challenges-camera-centric
Open access. Every entry in the Proceedings is free to read, with no charge to authors and no charge to readers. Slides are served from this site, so they stay available at the paper's permanent address.
Cite this paper
Anushree Nagvekar (2026). LiDAR Challenges in Camera-Centric Perception. Proceedings of the Raptors Conference, Vol. 1 (2026). Hackathon Raptors, London. https://doi.org/10.68495/rc.2026.C049
@inproceedings{raptors2026-c049,
title = {{LiDAR Challenges in Camera-Centric Perception}},
author = {Nagvekar, Anushree},
booktitle = {Proceedings of the Raptors Conference},
volume = {1},
year = {2026},
publisher = {Hackathon Raptors},
address = {London, United Kingdom},
doi = {10.68495/rc.2026.C049},
url = {https://doi.org/10.68495/rc.2026.C049},
abstract = {LiDAR provides essential 3D geometry for perception, but its role in camera-centric systems remains challenging due to sparse point clouds, limited semantic detail, weather sensitivity, and the cost of cross-sensor fusion. These issues can make tasks such as semantic segmentation and dense alignment more difficult, even though LiDAR still offers strong advantages for depth estimation, object separation, and tracking stability. The LiDAR reward: a 0.26 Gbps point cloud data replaces 3 Gbps of camera data plus 0.5–60 TFLOPS of depth reconstruction, with error flat across range. This talk covers selective fusion, adaptive attention, and compute-efficient processing to cut latency and bandwidth.}
}
TY - CONF TI - LiDAR Challenges in Camera-Centric Perception AU - Nagvekar, Anushree T2 - Proceedings of the Raptors Conference VL - 1 PY - 2026 DA - 2026/10/14 PB - Hackathon Raptors CY - London, United Kingdom DO - 10.68495/rc.2026.C049 UR - https://doi.org/10.68495/rc.2026.C049 AB - LiDAR provides essential 3D geometry for perception, but its role in camera-centric systems remains challenging due to sparse point clouds, limited semantic detail, weather sensitivity, and the cost of cross-sensor fusion. These issues can make tasks such as semantic segmentation and dense alignment more difficult, even though LiDAR still offers strong advantages for depth estimation, object separation, and tracking stability. The LiDAR reward: a 0.26 Gbps point cloud data replaces 3 Gbps of camera data plus 0.5–60 TFLOPS of depth reconstruction, with error flat across range. This talk covers selective fusion, adaptive attention, and compute-efficient processing to cut latency and bandwidth. LA - en ER -