3D目標檢測論文彙總（基於單目、雙目、點雲、Lidar、Radar）
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連結
一、單目影象下的3D目標檢測
1、YOLO3D
2、SSD-6D
3、3D Bounding Box Estimation Using Deep Learning and Geometry
4、GS3D：An Effcient 3D Object Detection Framework for Autonomous Driving
5、Deep MANTA： A Coarse-to-fine Many-Task Network for joint 2D and 3D vehicle analysis from monocular image
6、Task-Aware Monocular Depth Estimation for 3D Object Detection
7、M3D-RPN： Monocular 3D Region Proposal Network for Object Detection
8、Monocular 3D Object Detection with Pseudo-LiDAR Point Cloud
9、Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss
10、Disentangling Monocular 3D Object Detection
11、Shift R-CNN： Deep Monocular 3d Object Detection With Closed-Form Geometric Constraints
12、Monocular 3D Object Detection via Geometric Reasoning on Keypoints
13、Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction
14、Accurate Monocular Object Detection via Color-Embedded 3D Reconstruction for Autonomous Driving
15、3D Bounding Boxes for Road Vehicles： A One-Stage， Localization Prioritized Approach using Single Monocular Images
16、Orthographic Feature Transform for Monocular 3D Object Detection
17、Multi-Level Fusion based 3D Object Detection from Monocular Images
18、MonoGRNet： A Geometric Reasoning Network for Monocular 3D Object Localization
19、Mono3D++： Monocular 3D Vehicle Detection with Two-Scale 3D Hypotheses and Task Priors
二、基於融合的方法RGB影象+鐳射雷達/深度圖的3D目標檢測
1、AVOD
2、A General Pipeline for 3D Detection of Vehicles
3、Adaptive and Azimuth-Aware Fusion Network of Multimodal Local Features for 3D Object Detection
4、Deep Continuous Fusion for Multi-Sensor 3D Object Detection
5、Frustum PointNets for 3D Object Detection from RGB-D Data
6、Joint 3D Proposal Generation and Object Detection from View Aggregation
7、Multi-Task Multi-Sensor Fusion for 3D Object Detection
8、Multi-View 3D Object Detection Network for Autonomous Driving
9、PointFusion：Deep Sensor Fusion for 3D Bounding Box Estimation
10、Pseudo-LiDAR from Visual Depth Estimation：Bridging the Gap in 3D Object Detection for Autonomous Driving
三、基於鐳射雷達點雲的3D目標檢測
1、VoteNet
2、End-to-End Multi-View Fusion for 3D Object Detection in LiDAR Point Clouds
3、Deep Hough Voting for 3D Object Detection in Point Clouds
4、STD： Sparse-to-Dense 3D Object Detector for Point Cloud
5、PointPillars： Fast Encoders for Object Detection from Point Clouds
6、PointRCNN： 3D Object Proposal Generation and Detection from Point Cloud
7、PIXOR： Real-time 3D Object Detection from Point Clouds
8、Complex-YOLO： An Euler-Region-Proposal for Real-time 3D Object Detection on Point Clouds
9、YOLO3D： End-to-end real-time 3D Oriented Object Bounding Box Detection from LiDAR Point Cloud
10、Vehicle Detection from 3D Lidar Using FCN（百度早期工作2016年）
11、Object Detection and Classification in Occupancy Grid Maps using Deep Convolutional Networks
12、RT3D： Real-Time 3-D Vehicle Detection in LiDAR Point Cloud for Autonomous Driving
13、BirdNet： a 3D Object Detection Framework from LiDAR information
14、IPOD： Intensive Point-based Object Detector for Point Cloud
15、PIXOR： Real-time 3D Object Detection from Point Clouds
16、DepthCN： Vehicle Detection Using 3D-LIDAR and ConvNet
17、YOLO4D： A ST Approach for RT Multi-object Detection and Classification from LiDAR Point Clouds
18、PointRCNN： 3D Object Proposal Generation and Detection from Point Cloud
19、Part-A^2 Net： 3D Part-Aware and Aggregation Neural Network for Object Detection from Point Cloud
20、Voxel-FPN： multi-scale voxel feature aggregation in 3D object detection from point clouds
21、Fast Point RCNN
22、StarNet： Targeted Computation for Object Detection in Point Clouds
23、Class-balanced Grouping and Sampling for Point Cloud 3D Object Detection
24、LaserNet： An Efficient Probabilistic 3D Object Detector for Autonomous Driving
四、基於RGB-D影象下的3D目標檢測
1、Frustum PointNets for 3D Object Detection from RGB-D Data
2、Frustum VoxNet for 3D object detection from RGB-D or Depth images
五、基於立體視覺下的3D目標檢測
1、Object-Centric Stereo Matching for 3D Object Detection
2、Triangulation Learning Network： from Monocular to Stereo 3D Object Detection
3、Pseudo-LiDAR from Visual Depth Estimation： Bridging the Gap in 3D Object Detection for Autonomous Driving
4、Stereo R-CNN based 3D Object Detection for Autonomous Driving
六、基於Radar和RGB方式的3D檢測
CenterFusion： Center-based Radar and Camera Fusion for 3D Object Detection
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