Meta-RangeSeg: LiDAR Sequence Semantic Segmentation Using Multiple Feature Aggregation
Zhejiang University（浙江大学）；Alibaba-Zhejiang University Joint Research Institute of Frontier Technologies（阿里巴巴-浙江大学前沿技术联合研究院）；Hikvision Research Institute（海康威视研究院）
LiDAR sensor is essential to the perception system in autonomous vehicles and intelligent robots. To fulfill the real-time requirements in real-world applications, it is necessary to efficiently segment the LiDAR scans. Most of previous approaches directly project 3D point cloud onto the 2D spherical range image so that they can make use of the efficient 2D convolutional operations for image segmentation. Although having achieved the encouraging results, the neighborhood information is not well-preserved in the spherical projection. Moreover, the temporal information is not taken into consideration in the single scan segmentation task. To tackle these problems, we propose a novel approach to semantic segmentation for LiDAR sequences named Meta-RangeSeg, where a new range residual image representation is introduced to capture the spatial-temporal information. Specifically, Meta-Kernel is employed to extract the meta features, which reduces the inconsistency between the 2D range image coordinates input and 3D Cartesian coordinates output. An efficient U-Net backbone is used to obtain the multi-scale features. Furthermore, Feature Aggregation Module (FAM) strengthens the role of range channel and aggregates features at different levels. We have conducted extensive experiments for performance evaluation on SemanticKITTI and SemanticPOSS. The promising results show that our proposed Meta-RangeSeg method is more efficient and effective than the existing approaches. Our full implementation is publicly available at https://github.com/songw-zju/Meta-RangeSeg .
Semantic outdoor scene understanding based on 3D LiDAR point clouds is a challenging task for autonomous driving due to the sparse and irregular data structure. This paper takes advantages of the uneven range distribution of different LiDAR laser beams to propose a range aware instance segmentation network, RangeSeg. RangeSeg uses a shared encoder backbone with two range dependent decoders. A heavy decoder only computes top of a range image where the far and small objects locate to improve small object detection accuracy, and a light decoder computes whole range image for low computational cos…
Université Cote d’Azur（蔚蓝海岸大学）；NAPA-Technologies（NAPA技术公司）；Université Côte d’Azur（蔚蓝海岸大学）；University of Glasgow（格拉斯哥大学）；Sant’Anna School of Advanced Studies（圣安娜高等研究学院）
Deploying advanced imaging solutions to robotic and autonomous systems by mimicking human vision requires simultaneous acquisition of multiple fields of views, named the peripheral and fovea regions. Low-resolution peripheral field provides coarse scene exploration to direct the eye to focus to a highly resolved fovea region for sharp imaging. Among 3D computer vision techniques, Light Detection and Ranging (LiDAR) is currently considered at the industrial level for robotic vision. LiDAR is an imaging technique that monitors pulses of light at optical frequencies to sense the space and to reco…
Autonomous vehicles operate in a dynamic environment, where the speed with which a vehicle can perceive and react impacts the safety and efficacy of the system. LiDAR provides a prominent sensory modality that informs many existing perceptual systems including object detection, segmentation, motion estimation, and action recognition. The latency for perceptual systems based on point cloud data can be dominated by the amount of time for a complete rotational scan (e.g. 100 ms). This built-in data capture latency is artificial, and based on treating the point cloud as a camera image in order to …
Carnegie Mellon University（卡内基梅隆大学）；Bosch Research and Technology Center（博世研究与技术中心）
mmWave radars offer excellent depth resolution even at very long ranges owing to their high bandwidth. But their angular resolution is at least an order-of-magnitude worse than camera and lidar systems. Hence, mmWave radar is not a capable 3-D imaging solution in isolation. We propose Metamoran, a system that combines the complimentary strengths of radar and camera to obtain accurate, high resolution depth images over long ranges even in high clutter environments, all from a single fixed vantage point. Metamoran enables rich long-range depth imaging with applications in security and surveillan…
UAV Object Detection and Positioning in a Mining Industrial Metaverse with Custom Geo-Referenced Data
Department of Production and Management Engineering, Democritus University of Thrace（生产与管理工程系，德米特里乌斯·特拉基斯大学）；Department of Electrical and Computer Engineering, Democritus University of Thrace（电气与计算机工程系，德米特里乌斯·特拉基斯大学）
We present a novel post-processing tool for semantic segmentation of LiDAR point cloud data, called LidarMetaSeg, which estimates the prediction quality segmentwise. For this purpose we compute dispersion measures based on network probability outputs as well as feature measures based on point cloud input features and aggregate them on segment level. These aggregated measures are used to train a meta classification model to predict whether a predicted segment is a false positive or not and a meta regression model to predict the segmentwise intersection over union. Both models can then be applie…
Methods for the Segmentation of Reticular Structures Using 3D LiDAR Data: A Comparative Evaluation
Engineering Research Institute of Elche (I3E)（埃尔切工程研究所 (I3E)）；Valencian Graduate School and Research Network of Artificial Intelligence (ValgrAI)（瓦伦西亚人工智能研究生院与研究网络 (ValgrAI)）
Efficient and Privacy-Protecting Background Removal for 2D Video Streaming using iPhone 15 Pro Max LiDAR
Department of Computer Science, Iowa State University, Ames, IA, USA（计算机科学系，爱荷华州立大学）；Department of Computer Science and School of Education, Iowa State University, Ames, IA, USA（计算机科学系与教育学院，爱荷华州立大学）
本研究利用iPhone 15 Pro Max的LiDAR技术，结合GPU图像处理与Metal着色器实现60帧/秒的高效背景去除，该方法不受光照影响，仅受深度数据带宽和LiDAR材质反射局限，未来分辨率提升后有望成为主流背景去除方案。
MOVIN: Real-time Motion Capture using a Single LiDAR
MOVIN Inc.（MOVIN公司）；Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院（KAIST））；Gwangju Institute of Science and Technology (GIST)（光州科学技术院（GIST））
