Generating realistic and diverse LiDAR point clouds is crucial for autonomous driving simulation. Although previous methods achieve LiDAR point cloud generation from user inputs, they struggle to attain high-quality results while enabling versatile controllability, due to the imbalance between the complex distribution of LiDAR point clouds and the simple control signals. To address the limitation, we propose LiDARDraft, which utilizes the 3D layout to build a bridge between versatile conditional signals and LiDAR point clouds. The 3D layout can be trivially generated from various user inputs such as textual descriptions and images. Specifically, we represent text, images, and point clouds as unified 3D layouts, which are further transformed into semantic and depth control signals. Then, we employ a rangemap-based ControlNet to guide LiDAR point cloud generation. This pixel-level alignment approach demonstrates excellent performance in controllable LiDAR point clouds generation, enabling "simulation from scratch", allowing self-driving environments to be created from arbitrary textual descriptions, images and sketches.
3D object detection using LiDAR data is an indispensable component for autonomous driving systems. Yet, only a few LiDAR-based 3D object detection methods leverage segmentation information to further guide the detection process. In this paper, we propose a novel multi-task framework that jointly performs 3D object detection and panoptic segmentation. In our method, the 3D object detection backbone in Bird's-Eye-View (BEV) plane is augmented by the injection of Range-View (RV) feature maps from the 3D panoptic segmentation backbone. This enables the detection backbone to leverage multi-view inf…
ETH Zurich（苏黎世联邦理工学院）；Hesai Technology Co., Ltd.（禾赛科技有限公司）；EPFL（洛桑联邦理工学院）
The rapid development of autonomous driving and mobile mapping calls for off-the-shelf LiDAR SLAM solutions that are adaptive to LiDARs of different specifications on various complex scenarios. To this end, we propose MULLS, an efficient, low-drift, and versatile 3D LiDAR SLAM system. For the front-end, roughly classified feature points (ground, facade, pillar, beam, etc.) are extracted from each frame using dual-threshold ground filtering and principal components analysis. Then the registration between the current frame and the local submap is accomplished efficiently by the proposed multi-me…
RAVES-Calib: Robust, Accurate and Versatile Extrinsic Self Calibration Using Optimal Geometric Features
School of Systems Science and Engineering, Sun Yat-Sen University（系统科学与工程学院，中山大学）；Defense Innovation Institute, Chinese Academy of Military Science（国防创新研究院，中国人民解放军军事科学院）；Intelligent Game and Decision Laboratory（智能游戏与决策实验室）；College of Computer Science and Technology, Harbin Engineering University（计算机科学与技术学院，哈尔滨工程大学）
Institute for Intelligent Systems Research and Innovation (IISRI)（智能系统研究与创新研究所）；Deakin University（迪肯大学）
Point cloud data from 3D LiDAR sensors are one of the most crucial sensor modalities for versatile safety-critical applications such as self-driving vehicles. Since the annotations of point cloud data is an expensive and time-consuming process, therefore recently the utilisation of simulated environments and 3D LiDAR sensors for this task started to get some popularity. With simulated sensors and environments, the process for obtaining an annotated synthetic point cloud data became much easier. However, the generated synthetic point cloud data are still missing the artefacts usually exist in p…
Multi-Space Alignments Towards Universal LiDAR Segmentation
ShanghaiTech University（上海科技大学）；Shanghai AI Laboratory（上海人工智能实验室）；National University of Singapore（新加坡国立大学）；University of Hong Kong（香港大学）；East China Normal University（华东师范大学）；Nanyang Technological University（南洋理工大学）
Neural varifolds: an aggregate representation for quantifying the geometry of point clouds
School of Electronics and Computer Science, University of Southampton（南安普顿大学电子与计算机科学学院）；Department of Applied Mathematics and Theoretical Physics, University of Cambridge（剑桥大学应用数学与理论物理系）；Institut Camille Jordan, Université Claude Bernard Lyon 1（里昂第一大学卡米尔·约旦研究所）
Segment Any Point Cloud Sequences by Distilling Vision Foundation Models
Shanghai AI Laboratory（上海人工智能实验室）；National University of Singapore（新加坡国立大学）；The Hong Kong University of Science and Technology（香港科技大学）；The University of Hong Kong（香港大学）；Nanyang Technological University（南洋理工大学）
UniLION: Towards Unified Autonomous Driving Model with Linear Group RNNs
School of Electronic Information and Communications, Huazhong University of Science and Technology (HUST), Wuhan, China（华中科技大学电子信息与通信学院）；School of Software Engineering, Huazhong University of Science and Technology (HUST), Wuhan, China（华中科技大学软件工程学院）；Department of Computer Science, The University of Hong Kong, Hong Kong (HKU) SAR, China（香港大学计算机系）；Baidu Inc., Beijing, China（百度公司）
