PointPillars Backbone Type Selection For Fast and Accurate LiDAR Object Detection

• URL: http://arxiv.org/abs/2209.15252v1
• Date: Fri, 30 Sep 2022 06:18:14 GMT
• Title: PointPillars Backbone Type Selection For Fast and Accurate LiDAR Object Detection
• Authors: Konrad Lis, Tomasz Kryjak
• Abstract summary: We present the results of experiments on the impact of backbone selection of a deep convolutional neural network on detection accuracy and speed. We chose the PointPillars network, which is characterised by a simple architecture, high speed, and modularity that allows for easy expansion.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 3D object detection from LiDAR sensor data is an important topic in the context of autonomous cars and drones. In this paper, we present the results of experiments on the impact of backbone selection of a deep convolutional neural network on detection accuracy and computation speed. We chose the PointPillars network, which is characterised by a simple architecture, high speed, and modularity that allows for easy expansion. During the experiments, we paid particular attention to the change in detection efficiency (measured by the mAP metric) and the total number of multiply-addition operations needed to process one point cloud. We tested 10 different convolutional neural network architectures that are widely used in image-based detection problems. For a backbone like MobilenetV1, we obtained an almost 4x speedup at the cost of a 1.13% decrease in mAP. On the other hand, for CSPDarknet we got an acceleration of more than 1.5x at an increase in mAP of 0.33%. We have thus demonstrated that it is possible to significantly speed up a 3D object detector in LiDAR point clouds with a small decrease in detection efficiency. This result can be used when PointPillars or similar algorithms are implemented in embedded systems, including SoC FPGAs. The code is available at https://github.com/vision-agh/pointpillars\_backbone.

Related papers

• FastPillars: A Deployment-friendly Pillar-based 3D Detector [63.0697065653061]
  Existing BEV-based (i.e., Bird Eye View) detectors favor sparse convolutions (known as SPConv) to speed up training and inference. FastPillars delivers state-of-the-art accuracy on Open dataset with 1.8X speed up and 3.8 mAPH/L2 improvement over CenterPoint (SPConv-based)
  arXiv  Detail & Related papers  (2023-02-05T12:13:27Z)
• A Lightweight and Detector-free 3D Single Object Tracker on Point Clouds [50.54083964183614]
  It is non-trivial to perform accurate target-specific detection since the point cloud of objects in raw LiDAR scans is usually sparse and incomplete. We propose DMT, a Detector-free Motion prediction based 3D Tracking network that totally removes the usage of complicated 3D detectors.
  arXiv  Detail & Related papers  (2022-03-08T17:49:07Z)
• PiFeNet: Pillar-Feature Network for Real-Time 3D Pedestrian Detection from Point Cloud [64.12626752721766]
  We present PiFeNet, an efficient real-time 3D detector for pedestrian detection from point clouds. We address two challenges that 3D object detection frameworks encounter when detecting pedestrians: low of pillar features and small occupation areas of pedestrians in point clouds. Our approach is ranked 1st in KITTI pedestrian BEV and 3D leaderboards while running at 26 frames per second (FPS), and achieves state-of-the-art performance on Nuscenes detection benchmark.
  arXiv  Detail & Related papers  (2021-12-31T13:41:37Z)
• Embracing Single Stride 3D Object Detector with Sparse Transformer [63.179720817019096]
  In LiDAR-based 3D object detection for autonomous driving, the ratio of the object size to input scene size is significantly smaller compared to 2D detection cases. Many 3D detectors directly follow the common practice of 2D detectors, which downsample the feature maps even after quantizing the point clouds. We propose Single-stride Sparse Transformer (SST) to maintain the original resolution from the beginning to the end of the network.
  arXiv  Detail & Related papers  (2021-12-13T02:12:02Z)
• Deployment of Deep Neural Networks for Object Detection on Edge AI Devices with Runtime Optimization [11.408144862469172]
  We consider the deployment of two representative object detection networks on an edge AI platform. In particular, we consider RetinaNet for image-based 2D object detection and PointPillars for LiDAR-based 3D object detection. We evaluate the runtime of the deployed algorithms using two different libraries,RT and TorchScript.
  arXiv  Detail & Related papers  (2021-08-18T14:21:53Z)
• Learning Semantic Segmentation of Large-Scale Point Clouds with Random Sampling [52.464516118826765]
  We introduce RandLA-Net, an efficient and lightweight neural architecture to infer per-point semantics for large-scale point clouds. The key to our approach is to use random point sampling instead of more complex point selection approaches. Our RandLA-Net can process 1 million points in a single pass up to 200x faster than existing approaches.
  arXiv  Detail & Related papers  (2021-07-06T05:08:34Z)
• RSN: Range Sparse Net for Efficient, Accurate LiDAR 3D Object Detection [44.024530632421836]
  Range Sparse Net (RSN) is a simple, efficient, and accurate 3D object detector. RSN predicts foreground points from range images and applies sparse convolutions on the selected foreground points to detect objects. RSN is ranked first in the leaderboard based on the APH/LEVEL 1 metrics for LiDAR-based pedestrian and vehicle detection.
  arXiv  Detail & Related papers  (2021-06-25T00:23:55Z)
• LiDAR R-CNN: An Efficient and Universal 3D Object Detector [20.17906188581305]
  LiDAR-based 3D detection in point cloud is essential in the perception system of autonomous driving. We present LiDAR R-CNN, a second stage detector that can generally improve any existing 3D detector. In particular, based on one variant of PointPillars, our method could achieve new state-of-the-art results with minor cost.
  arXiv  Detail & Related papers  (2021-03-29T03:01:21Z)
• PV-RCNN++: Point-Voxel Feature Set Abstraction With Local Vector Representation for 3D Object Detection [100.60209139039472]
  We propose the PointVoxel Region based Convolution Neural Networks (PVRCNNs) for accurate 3D detection from point clouds. Our proposed PV-RCNNs significantly outperform previous state-of-the-art 3D detection methods on both the Open dataset and the highly-competitive KITTI benchmark.
  arXiv  Detail & Related papers  (2021-01-31T14:51:49Z)
• Optimisation of the PointPillars network for 3D object detection in point clouds [1.1470070927586016]
  In this paper we present our research on the optimisation of a deep neural network for 3D object detection in a point cloud. We performed the experiments for the PointPillars network, which offers a reasonable compromise between detection accuracy and calculation complexity. This will allow for real-time LiDAR data processing with low energy consumption.
  arXiv  Detail & Related papers  (2020-07-01T13:50:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.