IGLOSS introduces a zero‑shot open‑vocabulary semantic segmentation approach for 3D automotive LiDAR data. The method generates prototype images from text to bridge the image‑text modality gap, then matches 3D point features with 2D image features derived from a Vision Foundation Model. It achieves state‑of‑the‑art results on nuScenes and SemanticKITTI, with code, models, and images publicly available.
By Nermin Samet, Gilles Puy, Renaud Marlet
SpaceCache is a training‑free feature cache that reuses intermediate features from point‑cloud transformers across consecutive frames, allowing tokens of previously seen regions to skip transformer blocks and only compute new tokens. The method limits cache entry age and evicts low‑confidence entries to maintain reliability, and it operates once per encoder stage at negligible cost. Experiments on outdoor and indoor datasets (nuScenes, Waymo, ScanNet) with Utonia and LitePT‑B backbones show up to 1.65× speedup with less than 1‑point mIoU loss, outperforming token‑merging approaches.
By Dongheon Yang, Beomseok Kang, Jae-Joon Kim
arXiv:2609.17413v1 Announce Type: new
Abstract: This paper investigates easy strategies to boost the performance of existing networks for lidar semantic scene completion (SSC) without requiring compl...
By Tetiana Martyniuk, Jonathan Seele, Alexandre Boulch, Gilles Puy, Renaud Marlet, Raoul de Charette
arXiv:2606. 08206v1 Announce Type: cross Abstract: We present SegmentAnyTreeV2, a sensor- and platform-agnostic framework for semantic and instance segmentation of forest point clouds.
By Maciej Wielgosz, Stefano Puliti, Rasmus Astrup
The paper studies Graph-Guided Token Merging (G2TM), a module that reduces token count in Vision Transformers. It evaluates G2TM across multiple segmentation frameworks and decoder types, finding that its performance gains are tied to the encoder rather than the decoder. The authors report consistent reductions in GFLOPs (22‑47%) and throughput improvements (up to 74%) on ADE20K, with optimal hyperparameters depending mainly on backbone pre‑training and target dataset.
By Victor Bercy, Martyna Poreba, Michal Szczepanski, Samia Bouchafa
arXiv:2609.09881v1 Announce Type: new
Abstract: Semantic segmentation for autonomous driving requires reliable detection of vulnerable road users (VRUs) despite heavy class imbalance. We introduce CL...
By Toomas Tahves, Mauro Bellone, Raivo Sell
The paper introduces LiDAR‑SAM2, a framework that converts the 2D video foundation model SAM2 into a scalable source of supervision for 4D LiDAR data. By projecting SAM2 video masks into multi‑view LiDAR space and aggregating them temporally, the method automatically generates temporally coherent LiDAR labels without human annotation. Experiments on SemanticKITTI show that these automatically produced semantic and panoptic labels achieve quality close to full human annotation, enabling models trained on them to approach the performance of fully supervised systems.
By Jihun Kim, Hyun-Kurl Jang, Hyemin Yang, Jinnyeong Yang, Hyeokjun Kweon, Kuk-Jin Yoon
Lang3DSeg introduces a point‑transformer backbone for open‑vocabulary, annotation‑free 3D LiDAR segmentation, trained from scratch without geometric pre‑training. It tackles noise from 2D‑to‑3D label projections by applying a class‑priority rule and truncating projected instances at depth gaps, thereby correcting depth‑ambiguity errors. The method achieves state‑of‑the‑art results on nuScenes (52.8 % mIoU) and SemanticKITTI (41.4 % mIoU) while operating in real‑time on a single LiDAR sweep.
By Cigdem Kokenoz, Amir Salarpour, Alkim Domeke, Christopher Salas, Pedram MohajerAnsari, Long Cheng, Mert D. Pes\'e, Bing Li
arXiv:2608.29609v1 Announce Type: new
Abstract: Semantic segmentation is a crucial task for understanding Mars, the most Earth-like planet in our solar system. However, it is challenging because the...
By Ming-Han Lee, Chi-Yeh Chen
arXiv:2510.10471v3 Announce Type: replace-cross
Abstract: Environmental perception systems are crucial for high-precision mapping and autonomous navigation, with LiDAR serving as a core sensor provid...
By Chuang Chen, Yi Lin, Bo Wang, Jing Hu, Xi Wu, Wenyi Ge
arXiv:2605. 17131v2 Announce Type: replace-cross Abstract: Point cloud stands as the most widely adopted format for representing 3D shapes and scenes due to its simplicity and geometric fidelity.
By Minhas Kamal, Hiranya Garbha Kumar, Balakrishnan Prabhakaran
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