LiDAR Challenges in Camera-Centric Perception
Abstract
LiDAR contributes valuable three-dimensional geometric information to perception, and in camera-centric systems it also brings difficulties: sparse point clouds, limited semantic detail, sensitivity to weather, and the complexity of fusing it with other modalities. This paper examines that tension directly. The same sparsity that makes semantic segmentation harder leaves the structured geometry useful elsewhere, improving tracking through better depth estimation, object separation and temporal consistency, so the sensor is neither straightforwardly helpful nor straightforwardly costly. The consequence for system design is that effective fusion tends to rely on selective candidate processing and adaptive attention rather than dense alignment across all modalities, since aligning everything spends compute on regions where LiDAR adds nothing. Readers take away an account of where LiDAR genuinely strengthens a camera-centric perception stack, and how to use it efficiently enough to lower the compute and bandwidth a perception system requires.
- 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 contributes valuable three-dimensional geometric information to perception, and in camera-centric systems it also brings difficulties: sparse point clouds, limited semantic detail, sensitivity to weather, and the complexity of fusing it with other modalities. This paper examines that tension directly. The same sparsity that makes semantic segmentation harder leaves the structured geometry useful elsewhere, improving tracking through better depth estimation, object separation and temporal consistency, so the sensor is neither straightforwardly helpful nor straightforwardly costly. The consequence for system design is that effective fusion tends to rely on selective candidate processing and adaptive attention rather than dense alignment across all modalities, since aligning everything spends compute on regions where LiDAR adds nothing. Readers take away an account of where LiDAR genuinely strengthens a camera-centric perception stack, and how to use it efficiently enough to lower the compute and bandwidth a perception system requires.}
}
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 contributes valuable three-dimensional geometric information to perception, and in camera-centric systems it also brings difficulties: sparse point clouds, limited semantic detail, sensitivity to weather, and the complexity of fusing it with other modalities. This paper examines that tension directly. The same sparsity that makes semantic segmentation harder leaves the structured geometry useful elsewhere, improving tracking through better depth estimation, object separation and temporal consistency, so the sensor is neither straightforwardly helpful nor straightforwardly costly. The consequence for system design is that effective fusion tends to rely on selective candidate processing and adaptive attention rather than dense alignment across all modalities, since aligning everything spends compute on regions where LiDAR adds nothing. Readers take away an account of where LiDAR genuinely strengthens a camera-centric perception stack, and how to use it efficiently enough to lower the compute and bandwidth a perception system requires. LA - en ER -