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The 16th International Conference on Fluid dynamic and Mechanical & Electrical Control Engineering(FdMECE2012)

作者： Hongke Wang Xiaofeng Wang College of Mathematical and Physical Sciences Chongqing University of Science and Technology

ISBN: (纸本)9783037855164

＜正＞3d reconstruction is used in applications such as virtual reality,digital cinematography and urban *** 3d registration is the important part of 3d reconstruction,which is one of outstanding and very basic problems in computer *** the paper,considering that there often exist a great number of planes in scenes,we show a planar-feature-based registration *** planar features from the range image are ***,we can compute the transformation by SVd between the two coordinate systems and achieve the registration of these two range images.

关键词： Image segmentation Planar fit 3d registration SVd

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PuzzleNet:Boundary-Aware Feature Matching for Non-Overlapping 3d Point Clouds Assembly

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Journal of Computer Science & Technology 2023年第3期38卷 492-509页

作者：刘昊宇郭建伟姜海勇刘彦超张晓鹏严冬明 State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of Sciences Beijing 100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China

We address the 3d shape assembly of multiple geometric pieces without overlaps, a scenario often encountered in 3d shape design, field archeology, and robotics. Existing methods depend on strong assumptions on the number of shape pieces and coherent geometry or semantics of shape pieces. despite raising attention to 3d registration with complex or low overlapping patterns, few methods consider shape assembly with rare overlaps. To address this problem, we present a novel framework inspired by solving puzzles, named PuzzleNet, which conducts multi-task learning by leveraging both 3d alignment and boundary information. Specifically, we design an end-to-end neural network based on a point cloud transformer with two-way branches for estimating rigid transformation and predicting boundaries simultaneously. The framework is then naturally extended to reassemble multiple pieces into a full shape by using an iterative greedy approach based on the distance between each pair of candidate-matched pieces. To train and evaluate PuzzleNet, we construct two datasets, named ModelPuzzle and dublinPuzzle, based on a real-world urban scan dataset (dublinCity) and a synthetic CAd dataset (ModelNet40) respectively. Experiments demonstrate our effectiveness in solving 3d shape assembly for multiple pieces with arbitrary geometry and inconsistent semantics. Our method surpasses state-of-the-art algorithms by more than 10 times in rotation metrics and four times in translation metrics.

关键词： shape assembly 3d registration geometric learning boundary feature point cloud

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Apple fruit size estimation using a 3d machine vision system

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Information Processing in Agriculture 2018年第4期5卷 498-503页

作者： A.Gongal M.Karkee S.Amatya Biological Systems Engineering department United States Center for Precision and Automated Agricultural SystemsUnited States

Estimation of fruit size in tree fruit crops is essential for selective robotic harvesting and crop-load *** vision systems for fruit detection and localization have been studied widely for robotic harvesting and crop-load ***,only a few studies have been carried out to estimate fruit size in orchards using machine vision *** study was carried out to develop a machine vision system consisting of a color CCd camera and a time-of-flight(TOF)light-based 3d camera for estimating apple size in tree *** a measure of fruit size,the major axis(longest axis)was estimated based on(i)the 3d coordinates of pixels on corresponding apple surfaces,and(ii)the 2d size of individual pixels within apple *** the 3d coordinates-based method,the distance between pairs of pixels within apple regions were calculated using 3d coordinates,and the maximum distance between all pixel pairs within an apple region was estimated to be the major *** accuracy of estimating the major axis using 3d coordinates was 69.1%.In the pixel-size-based method,the physical sizes of pixels were estimated using a calibration model developed based on pixel coordinates and the distance to pixels from the *** major axis length was then estimated by summing the size of individual pixels along the major axis of the *** accuracy of size estimation increased to 84.8%when the pixel size-based method was *** results showed the potential for estimating fruit size in outdoor environments using a 3d machine vision system.

关键词： Machine vision Fruit identification 3d registration Size estimation Major axis

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