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...
Abstract 本文对multi-sensor 3D 目标检测的multi-related task 进行了探索。提出了一个端到端的学习框架,可以实现在图像上的2D 和 3D 的检测,并且包括目标的深度信息。 Introduction 单一的传感器成像各自有各…
image fusion,object detectionIn this paper we propose to exploit multiple related tasks for accurate multi-sensor 3D object detection. Towards this goal we present an end-to-end learnable architecture that reasons about 2D and 3D object detection as well as ground estimation and depth completion. ...
Implementation of Multi-Task Multi-Sensor Fusion for 3D Object Detection Introduction This project is a pytorch implementation of Multi-Task Multi-Sensor Fusion for 3D Object Detection paper, which is a end-to-end network to predict 3D bounding boxes using LIDAR point cloud and images. ...
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...
Aiming at the problem of multi-object detection such as target occlusion and tiny targets in road scenes, this paper proposes an improved YOLOv5 multi-object detection model based on ML-AFP (multi-level aggregation feature perception) mechanism. Since ti
Detection results on the testing set are submitted to the KITTI evaluation server for evaluation. The 11-point AP is used as the official metric. For 3D object detection task, 3D Intersection-Over-Union (IoU) is used to distinguish between true positive and false positive with a threshold of...
BEVDet: High-performance Multi-camera 3D Object Detection in Bird-Eye-View Zotero Abstract Autonomous driving perceives its surroundings for decision making, which is one of the most complex scenarios in visual perception. The success of paradigm innovation in solving the 2D object detection task in...
[ICCV 2023] SparseFusion: Fusing Multi-Modal Sparse Representations for Multi-Sensor 3D Object Detection - yichen928/SparseFusion
task network adept at three vital autonomous driving tasks: monocular 3D object detection, semantic segmentation, and dense depth estimation. To counter the challenge of negative transfer, which is the prevalent issue in multi-task learning, we introduce a task-adaptive attention generator. This ...