LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving
| Autor: | Carl Wellington, Carlos Vallespi-Gonzalez, Eric Kee, Ankit Laddha, Gregory P. Meyer |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning business.industry Computer science Computer Vision and Pattern Recognition (cs.CV) 010401 analytical chemistry Detector Real-time computing Probabilistic logic Computer Science - Computer Vision and Pattern Recognition 010501 environmental sciences Object (computer science) 01 natural sciences Object detection Machine Learning (cs.LG) 0104 chemical sciences Computer Science - Robotics Range (statistics) Benchmark (computing) Point (geometry) Artificial intelligence business Robotics (cs.RO) 0105 earth and related environmental sciences |
| Zdroj: | CVPR |
| Popis: | In this paper, we present LaserNet, a computationally efficient method for 3D object detection from LiDAR data for autonomous driving. The efficiency results from processing LiDAR data in the native range view of the sensor, where the input data is naturally compact. Operating in the range view involves well known challenges for learning, including occlusion and scale variation, but it also provides contextual information based on how the sensor data was captured. Our approach uses a fully convolutional network to predict a multimodal distribution over 3D boxes for each point and then it efficiently fuses these distributions to generate a prediction for each object. Experiments show that modeling each detection as a distribution rather than a single deterministic box leads to better overall detection performance. Benchmark results show that this approach has significantly lower runtime than other recent detectors and that it achieves state-of-the-art performance when compared on a large dataset that has enough data to overcome the challenges of training on the range view. Accepted for publication at CVPR 2019 |
| Databáze: | OpenAIRE |
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