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SAVTrack introduces a selective vote aggregation strategy for 3D single object tracking in LiDAR point clouds. By estimating the reliability of each candidate vote using local seed features and inter‑frame motion context, it removes low‑confidence hypotheses before clustering, thereby reducing noise in proposal formation. The method achieves competitive success and precision on KITTI and nuScenes while operating at 82 FPS and using fewer than one‑sixth of the votes required by dense aggregation.
arXiv:2609.07738v1 Announce Type: cross Abstract: LiDAR-based 3D Single Object Tracking (3D SOT) is critical for robotic perception and navigation and aims to localize dynamic objects across frames i...
RVLoss introduces a runoff vote mechanism for self‑supervised LiDAR scene flow estimation, addressing motion rigidity by grouping nearest‑neighbor derived motions into dominant flow candidates and selecting the most consistent one through a two‑stage voting process. This approach generates cluster‑wise rigid flows and free‑form flows as pseudo‑labels, enabling seamless integration into existing feedforward architectures. Experiments on the Argoverse2 2026 Challenge demonstrate that models trained with RVLoss outperform baseline self‑supervised methods by 20% and maintain consistent gains across four additional datasets.
arXiv:2506. 22784v2 Announce Type: replace-cross Abstract: Point-pixel registration between LiDAR point clouds and camera images is a fundamental yet challenging task in autonomous driving and robotic perception.
TrackCue is a self‑supervised framework that enhances LiDAR scene flow estimation by using dense image‑based point tracking to generate reliable static‑dynamic supervision. It anchors tracking queries to projected LiDAR points, applies a visually consistent motion compensation strategy to isolate true object motion, and lifts image‑space motion cues back to LiDAR points for refined pseudo‑labels. This approach reduces noise from sparse or occluded LiDAR geometry and improves the precision of dynamic pseudo‑labels, leading to better scene flow learning.
LiDAR-based 3D object detection is a core perception service for autonomous driving, but deployment requires robustness to point-cloud corruption while meeting latency and memory requirements. Existin...