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:2605.31177v2 Announce Type: replace
Abstract: Plain Transformers have become the de-facto architecture for processing text, audio, image, and video, offering a unified backbone for multimodal l...
By Gilles Puy, Nermin Samet, Alexandre Boulch, Spyros Gidaris, Tuan-Hung VU, Renaud Marlet
arXiv:2603.06168v4 Announce Type: replace
Abstract: Semantic segmentation across visual modalities such as 3D point clouds and panoramic images remains a challenging task, primarily due to the scarci...
By Sandeep Inuganti, Hideaki Kanayama, Kanta Shimizu, Mahdi Chamseddine, Soichiro Yokota, Didier Stricker, Jason Rambach
AutoExpert proposes a new benchmark for automating 3D LiDAR annotation using expert‑crafted guidelines, redefining evaluation protocols and re‑annotating nuScenes and PandaSet datasets. The pipeline combines 2D detection and segmentation in RGB images, lifts these detections into 3D with known sensor poses, and generates 3D cuboids, improving 3D detection mAP from 12.1 to 25.4 on the AutoExpert‑nuScenes benchmark.
By Yechi Ma, Wei Hua, Shu Kong
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
arXiv:2404.09431v3 Announce Type: replace
Abstract: Pseudo-LiDAR has become a promising paradigm for monocular 3D object detection by transforming monocular images into point cloud representations th...
By Bonan Ding, Jin Xie, Jing Nie, Jiale Cao, Yanwei Pang
The paper introduces PLOVIS, a data‑efficient framework for semantic segmentation of 3D point clouds that tackles three simultaneous data shortages: few training scenes, sparse point‑level labels, and missing 2D image sequences. PLOVIS generates pseudo‑labels by applying an open‑vocabulary image segmentation model directly to 3D point clouds, then refines these labels through a two‑stage filtering process and a class‑balanced memory bank. Experiments on ScanNet, S3DIS, Toronto3D, and Semantic3D show that PLOVIS outperforms existing fine‑tuning and weakly supervised methods under realistic, data‑scarce conditions.
By Takahiko Furuya
RaSFNet introduces a Ray-aligned and Scan-aware Fusion Network for LiDAR semantic segmentation, addressing limitations of projection-based methods by preserving geometric differences within each frustum and modeling spatial relationships across scanning regions. It employs a Ray-Aligned Geometric Distribution Encoding (RAGDE) module, a Scan-Topology-Aware Mamba (STAM) block, and a Range-Constrained Point-Frustum Fusion (RCPFF) mechanism to integrate point cloud geometry, LiDAR scanning characteristics, and range information. The network achieves competitive mIoU scores of 70.2% on SemanticKITTI and 77.7% on nuScenes validation sets.
By Chuang Chen
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:2610.19786v1 Announce Type: new
Abstract: Generating LiDAR point clouds from multi-view camera images is a valuable task with applications in controllable data synthesis, cross-modal simulation...
By Sizhuo Zhou, Xiaosong Jia, Fanrui Zhang, Qifeng Li, Junjie Li, Zirui Wang, Yukang Feng, Yu Hong, Shaofeng Zhang, Wenlong Liao, Tao He, Juyong Zhang, Junchi Yan
The paper introduces an open‑vocabulary 3D object detection pipeline that uses a promptable segmentation model (SAM3) to generate instance masks from six surround‑view cameras. These masks are converted into metric 3D boxes, achieving up to 0.413 mAP/0.555 NDS without any training when supervised box geometry is borrowed at inference. The approach also improves a supervised LiDAR‑only detector by 0.034 mAP through a camera‑witness rule, demonstrating that measurement precision, not 2D detection, limits performance.
By \"Omer Faruk Deniz, Mustafa Taha Ko\c{c}yi\u{g}it
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