这篇MMF(Multi-Task Multi-Sensor Fusion for 3D Object Detection[1])是Uber跟Toronto大学联合发布在CVPR2019的一篇关于利用多传感器(lidar+camera)融合进行物体识别的文章。 LiDAR跟Camera是自动驾驶车上非常常见的传感器,它们各自有自己的优缺点,例如: Camera 能够提供非常丰富的语义信息,而且能够看到很远的物体(例如2...
3.Multi-task Multi-sensor Detector 如图所示,为作者提出的端到端的多任务和多传感器结合的3D检测器的体系结构。(其中虚线箭头表示投影,实心箭头表示数据流)。作者提出的模型是一个简化的两级检测器。其具有Dense Fusion的双流多传感器骨干网络。第一阶段single-shot检测器,可输出少量高质量的3D检测结果。第二阶段应...
OBJECT recognition (Computer vision)TRANSFORMER modelsMULTISENSOR data fusionAUTONOMOUS vehiclesMulti-modal sensors are the key to ensuring the robust and accurate operation of autonomous driving systems, where LiDAR and cameras are important on-board sensors. However, current fusion methods...
题目:Deep Continuous Fusion for Multi-Sensor 3D Object Detection 来自:Uber: Ming Liang Note: 没有代码,主要看思想吧,毕竟是第一篇使用RGB feature maps 融合到BEV特征中; 从以下几个方面开始简述论文 Open Problems Contributions Methods Experiments My Conclusion 1> Open Problems 联合多传感器数据能...
3D object detection benchmarks by comparing them with different baselines. The experimental results show that the proposed fusion strategies can significantly improve the detection performance compared to the methods using only point clouds and the methods using only 2D semantic information. Most ...
Deep Continuous Fusion for Multi-Sensor 3D Object Detection Ming Liang1, Bin Yang1,2, Shenlong Wang1,2, and Raquel Urtasun1,2 1 Uber Advanced Technologies Group 2 University of Toronto {ming.liang, byang10, slwang, urtasun}@uber.com Abstract. In this paper, we propose a novel 3D objec...
We present MCF3D, a multi-stage complementary fusion three-dimensional (3D) object detection network for autonomous driving, robot navigation, and virtual reality. This is an end-to-end learnable architecture, which takes both LIDAR point clouds and RGB images as inputs and utilizes a 3D region...
preprocessing-open3d.ipynb Add Sparse depth map calculation. Oct 30, 2019 preprocessing.ipynb Add Sparse depth map calculation. Oct 30, 2019 Implementation of Multi-Task Multi-Sensor Fusion for 3D Object Detection Introduction This project is a pytorch implementation of Multi-Task Multi-Sensor Fusion...
Multi-sensor Fusion for 3D Detection F-PointNet 提出了一个级联的方法来进行multi-sensor 融合,即使用3D frustum 将已经检测好的图像上的2D 投影到3D frustum视野中,来得到depth ground box。PointNet 采用回归的方式来获得3D位置和形状。但是从point cloud中回归的方法很难处理occluded 目标和远处的目标。ROI级别的...
现在来看最终是如何进行Multi-view Fusion的. 整个网络结构如下 个人理解如下,虽然DV的模式不同,但是因为point的点数是一样的,因此在最终concat之前,转到 point-level时,数量应该是相同的,所以还能够进行concat. 另外个人觉得BEV视角和Perspective View 综合来做,也是替代3d卷积的一种方式....