Anytime-Lidar: Deadline-aware 3D Object Detection

• URL: http://arxiv.org/abs/2208.12181v1
• Date: Thu, 25 Aug 2022 16:07:10 GMT
• Title: Anytime-Lidar: Deadline-aware 3D Object Detection
• Authors: Ahmet Soyyigit, Shuochao Yao, Heechul Yun
• Abstract summary: We propose a scheduling algorithm, which intelligently selects the subset of the components to make effective time and accuracy trade-off on the fly. We apply our approach to a state-of-art 3D object detection network, PointPillars, and evaluate its performance on Jetson Xavier AGX dataset.
• Score: 5.491655566898372
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In this work, we present a novel scheduling framework enabling anytime perception for deep neural network (DNN) based 3D object detection pipelines. We focus on computationally expensive region proposal network (RPN) and per-category multi-head detector components, which are common in 3D object detection pipelines, and make them deadline-aware. We propose a scheduling algorithm, which intelligently selects the subset of the components to make effective time and accuracy trade-off on the fly. We minimize accuracy loss of skipping some of the neural network sub-components by projecting previously detected objects onto the current scene through estimations. We apply our approach to a state-of-art 3D object detection network, PointPillars, and evaluate its performance on Jetson Xavier AGX using nuScenes dataset. Compared to the baselines, our approach significantly improve the network's accuracy under various deadline constraints.

Related papers

• VALO: A Versatile Anytime Framework for LiDAR-based Object Detection Deep Neural Networks [4.953750672237398]
  This work addresses the challenge of adapting dynamic deadline requirements for LiDAR object detection deep neural networks (DNNs) We introduce VALO (Versatile Anytime algorithm for LiDAR Object detection), a novel data-centric approach that enables anytime computing of 3D LiDAR object detection DNNs. We implement VALO on state-of-the-art 3D LiDAR object detection networks, namely CenterPoint and VoxelNext, and demonstrate its dynamic adaptability to a wide range of time constraints.
  arXiv  Detail & Related papers  (2024-09-17T20:30:35Z)
• V-DETR: DETR with Vertex Relative Position Encoding for 3D Object Detection [73.37781484123536]
  We introduce a highly performant 3D object detector for point clouds using the DETR framework. To address the limitation, we introduce a novel 3D Relative Position (3DV-RPE) method. We show exceptional results on the challenging ScanNetV2 benchmark.
  arXiv  Detail & Related papers  (2023-08-08T17:14:14Z)
• Rethinking Voxelization and Classification for 3D Object Detection [68.8204255655161]
  The main challenge in 3D object detection from LiDAR point clouds is achieving real-time performance without affecting the reliability of the network. We present a solution to improve network inference speed and precision at the same time by implementing a fast dynamic voxelizer. In addition, we propose a lightweight detection sub-head model for classifying predicted objects and filter out false detected objects.
  arXiv  Detail & Related papers  (2023-01-10T16:22:04Z)
• AGO-Net: Association-Guided 3D Point Cloud Object Detection Network [86.10213302724085]
  We propose a novel 3D detection framework that associates intact features for objects via domain adaptation. We achieve new state-of-the-art performance on the KITTI 3D detection benchmark in both accuracy and speed.
  arXiv  Detail & Related papers  (2022-08-24T16:54:38Z)
• CVFNet: Real-time 3D Object Detection by Learning Cross View Features [11.402076835949824]
  We present a real-time view-based single stage 3D object detector, namely CVFNet. We first propose a novel Point-Range feature fusion module that deeply integrates point and range view features in multiple stages. Then, a special Slice Pillar is designed to well maintain the 3D geometry when transforming the obtained deep point-view features into bird's eye view.
  arXiv  Detail & Related papers  (2022-03-13T06:23:18Z)
• Semi-supervised 3D Object Detection via Temporal Graph Neural Networks [17.90796183565084]
  3D object detection plays an important role in autonomous driving and other robotics applications. We propose leveraging large amounts of unlabeled point cloud videos by semi-supervised learning of 3D object detectors. Our method achieves state-of-the-art detection performance on the challenging nuScenes and H3D benchmarks.
  arXiv  Detail & Related papers  (2022-02-01T02:06:54Z)
• 3D-FCT: Simultaneous 3D Object Detection and Tracking Using Feature Correlation [0.0]
  3D-FCT is a Siamese network architecture that utilizes temporal information to simultaneously perform the related tasks of 3D object detection and tracking. Our proposed method is evaluated on the KITTI tracking dataset where it is shown to provide an improvement of 5.57% mAP over a state-of-the-art approach.
  arXiv  Detail & Related papers  (2021-10-06T06:36:29Z)
• Delving into Localization Errors for Monocular 3D Object Detection [85.77319416168362]
  Estimating 3D bounding boxes from monocular images is an essential component in autonomous driving. In this work, we quantify the impact introduced by each sub-task and find the localization error' is the vital factor in restricting monocular 3D detection.
  arXiv  Detail & Related papers  (2021-03-30T10:38:01Z)
• Fast and Furious: Real Time End-to-End 3D Detection, Tracking and Motion Forecasting with a Single Convolutional Net [93.51773847125014]
  We propose a novel deep neural network that is able to jointly reason about 3D detection, tracking and motion forecasting given data captured by a 3D sensor. Our approach performs 3D convolutions across space and time over a bird's eye view representation of the 3D world.
  arXiv  Detail & Related papers  (2020-12-22T22:43:35Z)
• Generative Sparse Detection Networks for 3D Single-shot Object Detection [43.91336826079574]
  3D object detection has been widely studied due to its potential applicability to many promising areas such as robotics and augmented reality. Yet, the sparse nature of the 3D data poses unique challenges to this task. We propose Generative Sparse Detection Network (GSDN), a fully-convolutional single-shot sparse detection network.
  arXiv  Detail & Related papers  (2020-06-22T15:54:24Z)
• PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection [76.30585706811993]
  We present a novel and high-performance 3D object detection framework, named PointVoxel-RCNN (PV-RCNN) Our proposed method deeply integrates both 3D voxel Convolutional Neural Network (CNN) and PointNet-based set abstraction. It takes advantages of efficient learning and high-quality proposals of the 3D voxel CNN and the flexible receptive fields of the PointNet-based networks.
  arXiv  Detail & Related papers  (2019-12-31T06:34:10Z)

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.