GOLD-BEV learns dense BEV semantic maps including dynamic agents from ego-centric sensors by using synchronized aerial imagery for training supervision and pseudo-label generation.
Joint 3D Proposal Generation and Object Detection from View Aggregation
3 Pith papers cite this work. Polarity classification is still indexing.
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abstract
We present AVOD, an Aggregate View Object Detection network for autonomous driving scenarios. The proposed neural network architecture uses LIDAR point clouds and RGB images to generate features that are shared by two subnetworks: a region proposal network (RPN) and a second stage detector network. The proposed RPN uses a novel architecture capable of performing multimodal feature fusion on high resolution feature maps to generate reliable 3D object proposals for multiple object classes in road scenes. Using these proposals, the second stage detection network performs accurate oriented 3D bounding box regression and category classification to predict the extents, orientation, and classification of objects in 3D space. Our proposed architecture is shown to produce state of the art results on the KITTI 3D object detection benchmark while running in real time with a low memory footprint, making it a suitable candidate for deployment on autonomous vehicles. Code is at: https://github.com/kujason/avod
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Voxel-FPN proposes an encoder-decoder architecture for multi-scale voxel feature aggregation in one-stage 3D object detection from point clouds, reporting competitive speed and accuracy on KITTI-3D.
Proposes weighted self-incremental transfer learning to address class imbalance in 3D point cloud semantic segmentation and reports a new benchmark on the KITTI dataset.
citing papers explorer
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GOLD-BEV: GrOund and aeriaL Data for Dense Semantic BEV Mapping of Dynamic Scenes
GOLD-BEV learns dense BEV semantic maps including dynamic agents from ego-centric sensors by using synchronized aerial imagery for training supervision and pseudo-label generation.
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Voxel-FPN: multi-scale voxel feature aggregation in 3D object detection from point clouds
Voxel-FPN proposes an encoder-decoder architecture for multi-scale voxel feature aggregation in one-stage 3D object detection from point clouds, reporting competitive speed and accuracy on KITTI-3D.
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End-to-End 3D-PointCloud Semantic Segmentation for Autonomous Driving
Proposes weighted self-incremental transfer learning to address class imbalance in 3D point cloud semantic segmentation and reports a new benchmark on the KITTI dataset.