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GeeNet:robust and fast point cloud completion for ground elevation estimation towards autonomous vehicles

Frontiers of Information Technology & Electronic Engineering 2024年第7期25卷 938-950页

作者： Liwen LIU Weidong YANG Ben FEI Shanghai Key Laboratory of Data Science School of Computer ScienceFudan UniversityShanghai 200433China Zhuhai Fudan Innovation Institute Zhuhai 519000China

Ground elevation estimation is vital for numerous applications in autonomous vehicles and intelligent robotics including three-dimensional object detection,navigable space detection,point cloud matching for localization,and registration for ***,most works regard the ground as a plane without height information,which causes inaccurate manipulation in these *** this work,we propose GeeNet,a novel end-to-end,lightweight method that completes the ground in nearly real time and simultaneously estimates the ground elevation in a grid-based *** leverages the mixing of two-and three-dimensional convolutions to preserve a lightweight architecture to regress ground elevation information for each cell of the *** the first time,GeeNet has fulfilled ground elevation estimation from semantic scene *** use the SemanticKITTI and SemanticPOSS datasets to validate the proposed GeeNet,demonstrating the qualitative and quantitative performances of GeeNet on ground elevation estimation and semantic scene completion of the point ***,the crossdataset generalization capability of GeeNet is experimentally *** achieves state-of-the-art performance in terms of point cloud completion and ground elevation estimation,with a runtime of 0.88 ms.

关键词： point cloud completion Ground elevation estimation Real-time Autonomous vehicles

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HDR-Net-Fusion:Real-time 3D dynamic scene reconstruction with a hierarchical deep reinforcement network

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Computational Visual Media 2021年第4期7卷 419-435页

作者： Hao-Xuan Song Jiahui Huang Yan-Pei Cao Tai-Jiang Mu BNRist Department of Computer Science and TechnologyTsinghua UniversityBeiing 100084China Kuaishou Technology Co. Ltd.Beijing 100085China

Reconstructing dynamic scenes with commodity depth cameras has many applications in computer graphics,computer vision,and ***,due to the presence of noise and erroneous observations from data capturing devices and the inherently ill-posed nature of non-rigid registration with insufficient information,traditional approaches often produce low-quality geometry with holes,bumps,and *** propose a novel 3D dynamic reconstruction system,named HDR-Net-Fusion,which learns to simultaneously reconstruct and refine the geometry on the fly with a sparse embedded deformation graph of surfels,using a hierarchical deep reinforcement(HDR)*** latter comprises two parts:a global HDR-Net which rapidly detects local regions with large geometric errors,and a local HDR-Net serving as a local patch refinement operator to promptly complete and enhance such *** the global HDR-Net is formulated as a novel reinforcement learning problem to implicitly learn the region selection strategy with the goal of improving the overall reconstruction *** applicability and efficiency of our approach are demonstrated using a large-scale dynamic reconstruction *** method can reconstruct geometry with higher quality than traditional methods.

关键词： dynamic 3D scene reconstruction deep reinforcement learning point cloud completion deep neural networks

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