3D Object Detection From LiDAR Data Using Distance Dependent Feature Extraction
arXiv:2003.00888 · doi:10.5220/0009330402890300
Abstract
This paper presents a new approach to 3D object detection that leverages the properties of the data obtained by a LiDAR sensor. State-of-the-art detectors use neural network architectures based on assumptions valid for camera images. However, point clouds obtained from LiDAR are fundamentally different. Most detectors use shared filter kernels to extract features which do not take into account the range dependent nature of the point cloud features. To show this, different detectors are trained on two splits of the KITTI dataset: close range (objects up to 25 meters from LiDAR) and long-range. Top view images are generated from point clouds as input for the networks. Combined results outperform the baseline network trained on the full dataset with a single backbone. Additional research compares the effect of using different input features when converting the point cloud to image. The results indicate that the network focuses on the shape and structure of the objects, rather than exact values of the input. This work proposes an improvement for 3D object detectors by taking into account the properties of LiDAR point clouds over distance. Results show that training separate networks for close-range and long-range objects boosts performance for all KITTI benchmark difficulties.
arXiv comment10 pages, 8 figures, 6th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS 2020)
References in corpus (14)
- Deep Residual Learning for Image Recognition
- R2CNN: Rotational Region CNN for Orientation Robust Scene Text Detection
- Feature Pyramid Networks for Object Detection
- Visualizing and Understanding Convolutional Networks
- Fast and Furious: Real Time End-to-End 3D Detection, Tracking and Motion Forecasting with a Single Convolutional Net
- VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
- HDNET: Exploiting HD Maps for 3D Object Detection
- PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud
- IPOD: Intensive Point-based Object Detector for Point Cloud
- From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network
- Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving
- Multi-View 3D Object Detection Network for Autonomous Driving
- The H3D Dataset for Full-Surround 3D Multi-Object Detection and Tracking in Crowded Urban Scenes
- PIXOR: Real-time 3D Object Detection from Point Clouds