Three-Dimensional Reconstruction Methods for Obstacles in Complex Parking Scenarios
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NETL
NSTL
万方数据
针对复杂智能泊车场景下对不规则障碍物的智能检测任务,提出一种采用网格化结构光投影检测区域,获取结构光网格在障碍物表面产生的形变,提高障碍物特征采集精度,并训练端到端网络生成深度图像的方法.进而融合RGB(Red green blue)图像的外形轮廓特征和深度图像的三维深度特征,提出RGB和深度图像的双特征并行处理算法,设计多尺度特征融合模型,在不增加模型复杂度的基础上实现多特征提取和深度融合,以更好地指导Mesh模型向真实三维模型转变.最终,以多尺度特征为输入建立基于图卷积神经网络的端到端三维重建模型.智能泊车场景实验结果表明,与基础三维重建模型相比,所提出的模型在查准距离误差和移动距离误差均值上分别降低了2%和9%;与三种主流三维重建模型相比,查准距离误差均值分别降低了60%、2%和78%,移动距离误差分别降低了16%、23%和91%.
Detecting irregular obstacles under complex scenarios of intelligent parking is a difficult task.Therefore,a method that employs a gridded structured light projection for the detection area is proposed in this study.Specifically,this method captures the deformation of structured light grids on obstacle surfaces,thereby enhancing the precision of obstacle feature collection.In addition,a method for generating depth maps via the training of an end-to-end network is introduced.Subsequently,the fusion of external contour features from red green blue(RGB)images with three-dimensional(3D)depth features from depth images is achieved,culminating in the proposition of a dual-feature parallel processing algorithm for RGB and depth imagery.A multi-scale feature fusion extraction model is designed,facilitating multifaceted feature extraction and in-depth fusion without escalating model complexity,which enables the transition of mesh models towards accurate 3D representations.Consequently,a multi-scale feature-informed,graph convolutional neural network-based end-to-end 3D reconstruction model is established.Experimental results in intelligent parking scenarios indicate that compared to foundational 3D reconstruction models,the model proposed herein achieves a mean reduction of 2%and 9%in chamfer distance and earth mover's distance,respectively.Furthermore,relative to three mainstream 3D reconstruction models,the mean reduction in chamfer distance is 60%,2%,and 78%,respectively,while the reduction in earth mover's distance is 16%,23%,and 91%,respectively.
deep learningintelligent parking3D reconstructionmulti-scale feature fusion
NETL
NSTL
万方数据
