TriBand-BEV introduces a three-band height-aware BEV encoding of LiDAR data to enable single-pass real-time 3D detection of pedestrians, cars, and cyclists with improved KITTI accuracy.
Vehicle Detection from 3D Lidar Using Fully Convolutional Network
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abstract
Convolutional network techniques have recently achieved great success in vision based detection tasks. This paper introduces the recent development of our research on transplanting the fully convolutional network technique to the detection tasks on 3D range scan data. Specifically, the scenario is set as the vehicle detection task from the range data of Velodyne 64E lidar. We proposes to present the data in a 2D point map and use a single 2D end-to-end fully convolutional network to predict the objectness confidence and the bounding boxes simultaneously. By carefully design the bounding box encoding, it is able to predict full 3D bounding boxes even using a 2D convolutional network. Experiments on the KITTI dataset shows the state-of-the-art performance of the proposed method.
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A survey of RGB-D object detection from traditional hand-crafted features with machine learning to deep learning techniques.
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TriBand-BEV: Real-Time LiDAR-Only 3D Pedestrian Detection via Height-Aware BEV and High-Resolution Feature Fusion
TriBand-BEV introduces a three-band height-aware BEV encoding of LiDAR data to enable single-pass real-time 3D detection of pedestrians, cars, and cyclists with improved KITTI accuracy.
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RGB-D image-based Object Detection: from Traditional Methods to Deep Learning Techniques
A survey of RGB-D object detection from traditional hand-crafted features with machine learning to deep learning techniques.