ARLOT uses a least-squares graph over multi-agent bounding boxes to denoise adversarial detections and a two-stage Kalman tracking association to improve 3D multi-object tracking under point-cloud attacks.
A comprehensive study of the robustness for lidar- based 3d object detectors against adversarial attacks,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.CV 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
background 1representative citing papers
citing papers explorer
-
Robustifying 3D Perception via Least-Squares Graphs for Multi-Agent Object Tracking
ARLOT uses a least-squares graph over multi-agent bounding boxes to denoise adversarial detections and a two-stage Kalman tracking association to improve 3D multi-object tracking under point-cloud attacks.