Hugging Face Trending Papers
Aug 19

RVLoss: Runoff Vote Loss for Self-Supervised LiDAR Scene Flow Estimation

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 Computer Vision
Oct 5

LightLoc++: Sensor-Robust Representation Learning for Efficient Outdoor LiDAR Localization

LightLoc++ introduces a sensor‑robust framework for outdoor LiDAR localization that decouples a pretrained backbone from lightweight scene‑specific heads, enabling efficient new‑scene training. The authors present SULID, a synchronized urban multi‑LiDAR dataset featuring 32‑, 64‑, and 128‑beam sensors, which is used to pretrain a backbone via cross‑sensor consistency learning. Additional techniques such as sample classification guidance and redundant sample downsampling reduce regression ambiguity and computational load, achieving state‑of‑the‑art performance with minimal new‑scene training cost.

By Wen Li, Shangshu Yu, Dunqiang Liu, Shaoyang Chen, Qiming Xia, Sheng Ao, Siqi Shen, Chenglu Wen, Cheng Wang
arXiv AI
Jun 24

HilDA: Hierarchical Distillation with Diffusion for Advancing Self-Supervised LiDAR Pre-training

arXiv:2606. 20189v3 Announce Type: replace-cross Abstract: Leveraging Vision Foundation Models (VFMs) for camera-to-LiDAR knowledge distillation offers a promising solution to the scarcity of annotated data needed to represent the immense geometric and kinematic diversity of real-world autonomous driving (AD).

By Maciej Wozniak, Jesper Ericsson, Hariprasath Govindarajan, Truls Nyberg, Thomas Gustafsson, Patric Jensfelt, Olov Andersson
arXiv AI
Jun 19

HilDA: Hierarchical Distillation with Diffusion for Advancing Self-Supervised LiDAR Pre-trainin

arXiv:2606. 20189v1 Announce Type: cross Abstract: Leveraging Vision Foundation Models (VFMs) for camera-to-LiDAR knowledge distillation offers a promising solution to the scarcity of annotated data needed to represent the immense geometric and kinematic diversity of real-world autonomous driving (AD).

By Maciej Wozniak, Jesper Ericsson, Hariprasath Govindarajan, Truls Nyberg, Thomas Gustafsson, Patric Jensfelt, Olov Andersson