Zhou Y, Sun P, Zhang Y et al (2020) End-to-end multi-view fusion for 3d object detection in lidar point clouds[C]. In: Conference on robot learning. PMLR, pp 923–932 Zhou Y, Tuzel O (2018) Voxelnet: End-to-end learning for point cloud based 3d object detection[C]. In: Proce...
Building Point Detection from Vehicle-Borne LiDAR Data Based on Voxel Group and Horizontal Hollow Analysis In this paper, a new technical framework for automatic and efficient building point extraction is proposed, including three main steps: (1) voxel group-based shape recognition; (2) category-or...
Height distribution analysis, convexity constraints, morphological filtering, and moving-window voxel-based filters are exploited to detect object points. An object index is introduced and is computed by Otsu segmentation to label the classified lidar points i.e. indices above the threshold for objects...
Aiming at the above problems, this paper proposed a point cloud annular weld seam detection network based on deep learning, which improved the robustness of point cloud annular weld seam detection. 2 Related work 2.1 Point cloud acquisition Common point cloud acquisition methods include lidar [...
Voxel-based representation of 3D point clouds: Methods, applications, and its potential use in the construction industry 2021, Automation in Construction Show abstract Deep Learning for LiDAR Point Clouds in Autonomous Driving: A Review 2021, IEEE Transactions on Neural Networks and Learning Systems Cu...
In the perception task of autonomous driving, multi-modal methods have become a trend due to the complementary characteristics of LiDAR point clouds and im... B Ren,J Yin 被引量: 0发表: 2023年 3D Object Detection under Urban Road Traffic Scenarios Based on Dual-Layer Voxel Features Fusion ...
However, LiDAR sensors are expensive and less portable, while cameras are cheaper and provide richer visual cues of the driving scenes. This motivated the study of camera-based SSC solutions, as first proposed in the pioneering work of MonoScene [4]. MonoScene lifts 2D image inputs to 3D ...
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Ni et al. (2017) described three main strategies for LiDAR data classification with respect to granularity of the processing unit (irrespective of whether these are done with or without machine learning or deep learning techniques): (1) point-based classification (e.g. Yastikli and Cetin, ...
lidarMany remote sensing-based methods estimating forest biomass rely on allometric biomass models for field reference data. Terrestrial laser scanning (TLS) has emerged as a tool for detailed data collection in forestry applications, and the methods have been proposed to derive, e.g. tree position...