Birds Eye View Object Detection
8 papers with code • 22 benchmarks • 1 datasets
KITTI birds eye view detection task
Benchmarks
These leaderboards are used to track progress in Birds Eye View Object Detection
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KITTI
Most implemented papers
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
qianguih/voxelnet
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CVPR 2018
Accurate detection of objects in 3D point clouds is a central problem in many applications, such as autonomous navigation, housekeeping robots, and augmented/virtual reality.
PointPillars: Fast Encoders for Object Detection from Point Clouds
nutonomy/second.pytorch
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CVPR 2019
These benchmarks suggest that PointPillars is an appropriate encoding for object detection in point clouds.
Sparse PointPillars: Maintaining and Exploiting Input Sparsity to Improve Runtime on Embedded Systems
kylevedder/SparsePointPillars
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Bird's Eye View (BEV) is a popular representation for processing 3D point clouds, and by its nature is fundamentally sparse.
PIXOR: Real-time 3D Object Detection from Point Clouds
DerrickXuNu/OpenCOOD
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CVPR 2018
Existing approaches are, however, expensive in computation due to high dimensionality of point clouds.
Frustum-PointPillars: A Multi-Stage Approach for 3D Object Detection using RGB Camera and LiDAR
anshulpaigwar/Frustum-Pointpillars
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2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) 2021
We train our network on the KITTI dataset and perform experiments to show the effectiveness of our network.
CIA-SSD: Confident IoU-Aware Single-Stage Object Detector From Point Cloud
Existing single-stage detectors for locating objects in point clouds often treat object localization and category classification as separate tasks, so the localization accuracy and classification confidence may not well align.
SE-SSD: Self-Ensembling Single-Stage Object Detector From Point Cloud
Vegeta2020/SE-SSD
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CVPR 2021
Lastly, to better exploit hard targets, we design an ODIoU loss to supervise the student with constraints on the predicted box centers and orientations.
Accurate and Real-time 3D Pedestrian Detection Using an Efficient Attentive Pillar Network
ldtho/pifenet
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Efficiently and accurately detecting people from 3D point cloud data is of great importance in many robotic and autonomous driving applications.
