The task of detecting three-dimensional objects using only RGB images presents a considerable challenge within the domain of computer vision.The core issue lies in accurately performing epipolar geometry matching betw...The task of detecting three-dimensional objects using only RGB images presents a considerable challenge within the domain of computer vision.The core issue lies in accurately performing epipolar geometry matching between multiple views to obtain latent geometric priors.Existing methods establish correspondences along epipolar line features in voxel space through various layers of convolution.However,this step often occurs in the later stages of the network,which limits overall performance.To address this challenge,we introduce a novel framework,ImVoxelENet,that integrates a geometric epipolar constraint.We start from the back-projection of pixel-wise features and design an attention mechanism that captures the relationship between forward and backward features along the ray for multiple views.This approach enables the early establishment of geometric correspondences and structural connections between epipolar lines.Using ScanNetV2 as a benchmark,extensive comparative and ablation experiments demonstrate that our proposed network achieves a 1.1%improvement in mAP,highlighting its effectiveness in enhancing 3D object detection performance.Our code is available at https://github.com/xug-coder/ImVoxelENet.展开更多
In order to provide citizens with safe, convenient and comfortable services and infrastructure in a metropolis, the prediction of passenger flows in the metro-net of subway system has become more important than ever b...In order to provide citizens with safe, convenient and comfortable services and infrastructure in a metropolis, the prediction of passenger flows in the metro-net of subway system has become more important than ever before. Al- though a great number of prediction methods have been pre- sented in the field of transportation, all of them belong to the station oriented approach, which is not well suited to the Bei- jing subway system. This paper proposes a novel metro-net oriented method, called the probability tree based passenger flow model, which is also based on historic origin-destination （OD） information. First it learns and obtains the appearance probabilities for each kind of OD pair. For the real-time origin datum, the destination datum is calculated, and then several kinds of passenger flow in the metro-net can be pre- dicted by gathering all the contributions. The results of exper- iments, using the historical data of Beijing subway, show that although the proposed method has lower performance than existing prediction approaches for forecasting exit passenger flows, it is able to predict several additional kinds of passen- ger flow in stations and throughout the subway system; and it is a more feasible, suitable, and advanced passenger flow prediction model for Beijing subway system.展开更多
文摘The task of detecting three-dimensional objects using only RGB images presents a considerable challenge within the domain of computer vision.The core issue lies in accurately performing epipolar geometry matching between multiple views to obtain latent geometric priors.Existing methods establish correspondences along epipolar line features in voxel space through various layers of convolution.However,this step often occurs in the later stages of the network,which limits overall performance.To address this challenge,we introduce a novel framework,ImVoxelENet,that integrates a geometric epipolar constraint.We start from the back-projection of pixel-wise features and design an attention mechanism that captures the relationship between forward and backward features along the ray for multiple views.This approach enables the early establishment of geometric correspondences and structural connections between epipolar lines.Using ScanNetV2 as a benchmark,extensive comparative and ablation experiments demonstrate that our proposed network achieves a 1.1%improvement in mAP,highlighting its effectiveness in enhancing 3D object detection performance.Our code is available at https://github.com/xug-coder/ImVoxelENet.
基金This work was supported by the National High- Tech Research and Development Plan of China （863） （2011AA010502）, the National Natural Science Foundation of China （Grant No. 61103093）, the Doctoral Fund of Ministry of Education of China （20091102110017）, the International Science ＆ Technology Cooperation Program of China （2010DFB 13350）, the Supported Project （SKLSDE-2012ZX-16） of the State Key Laboratory of Software Development Environment, and the Fundamen- tal Research Funds for the Central Universities. We are thankful to Bei- jing Municipal Committee of Transportation, Beijing Metro Network Con- trol Center, Beijing Mass Transit Railway Operation Corporation Limited, and Beijing MTR Corporation for their great help.
文摘In order to provide citizens with safe, convenient and comfortable services and infrastructure in a metropolis, the prediction of passenger flows in the metro-net of subway system has become more important than ever before. Al- though a great number of prediction methods have been pre- sented in the field of transportation, all of them belong to the station oriented approach, which is not well suited to the Bei- jing subway system. This paper proposes a novel metro-net oriented method, called the probability tree based passenger flow model, which is also based on historic origin-destination （OD） information. First it learns and obtains the appearance probabilities for each kind of OD pair. For the real-time origin datum, the destination datum is calculated, and then several kinds of passenger flow in the metro-net can be pre- dicted by gathering all the contributions. The results of exper- iments, using the historical data of Beijing subway, show that although the proposed method has lower performance than existing prediction approaches for forecasting exit passenger flows, it is able to predict several additional kinds of passen- ger flow in stations and throughout the subway system; and it is a more feasible, suitable, and advanced passenger flow prediction model for Beijing subway system.
