SimPB: A Single Model for 2D and 3D Object Detection from Multiple Cameras

• URL: http://arxiv.org/abs/2403.10353v2
• Date: Wed, 17 Jul 2024 03:56:33 GMT
• Title: SimPB: A Single Model for 2D and 3D Object Detection from Multiple Cameras
• Authors: Yingqi Tang, Zhaotie Meng, Guoliang Chen, Erkang Cheng,
• Abstract summary: We present a single model termed SimPB, which simultaneously detects 2D objects in the perspective view and 3D objects in the BEV space from multiple cameras. A hybrid decoder consists of several multi-view 2D decoder layers and several 3D decoder layers, specifically designed for their respective detection tasks.
• Score: 3.648972014796591
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The field of autonomous driving has attracted considerable interest in approaches that directly infer 3D objects in the Bird's Eye View (BEV) from multiple cameras. Some attempts have also explored utilizing 2D detectors from single images to enhance the performance of 3D detection. However, these approaches rely on a two-stage process with separate detectors, where the 2D detection results are utilized only once for token selection or query initialization. In this paper, we present a single model termed SimPB, which simultaneously detects 2D objects in the perspective view and 3D objects in the BEV space from multiple cameras. To achieve this, we introduce a hybrid decoder consisting of several multi-view 2D decoder layers and several 3D decoder layers, specifically designed for their respective detection tasks. A Dynamic Query Allocation module and an Adaptive Query Aggregation module are proposed to continuously update and refine the interaction between 2D and 3D results, in a cyclic 3D-2D-3D manner. Additionally, Query-group Attention is utilized to strengthen the interaction among 2D queries within each camera group. In the experiments, we evaluate our method on the nuScenes dataset and demonstrate promising results for both 2D and 3D detection tasks. Our code is available at: https://github.com/nullmax-vision/SimPB.

Related papers

• Enhancing 3D Object Detection with 2D Detection-Guided Query Anchors [6.3557174349423455]
  We present a novel query generating approach termed QAF2D, which infers 3D query anchors from 2D detection results. The largest improvement that QAF2D can bring about on the nuScenes validation subset is $2.3%$ NDS and $2.7%$ mAP.
  arXiv  Detail & Related papers  (2024-03-10T04:38:27Z)
• Joint-MAE: 2D-3D Joint Masked Autoencoders for 3D Point Cloud Pre-training [65.75399500494343]
  Masked Autoencoders (MAE) have shown promising performance in self-supervised learning for 2D and 3D computer vision. We propose Joint-MAE, a 2D-3D joint MAE framework for self-supervised 3D point cloud pre-training.
  arXiv  Detail & Related papers  (2023-02-27T17:56:18Z)
• Bridged Transformer for Vision and Point Cloud 3D Object Detection [92.86856146086316]
  Bridged Transformer (BrT) is an end-to-end architecture for 3D object detection. BrT learns to identify 3D and 2D object bounding boxes from both points and image patches. We experimentally show that BrT surpasses state-of-the-art methods on SUN RGB-D and ScanNetV2 datasets.
  arXiv  Detail & Related papers  (2022-10-04T05:44:22Z)
• A Simple Baseline for Multi-Camera 3D Object Detection [94.63944826540491]
  3D object detection with surrounding cameras has been a promising direction for autonomous driving. We present SimMOD, a Simple baseline for Multi-camera Object Detection. We conduct extensive experiments on the 3D object detection benchmark of nuScenes to demonstrate the effectiveness of SimMOD.
  arXiv  Detail & Related papers  (2022-08-22T03:38:01Z)
• DETR3D: 3D Object Detection from Multi-view Images via 3D-to-2D Queries [43.02373021724797]
  We introduce a framework for multi-camera 3D object detection. Our method manipulates predictions directly in 3D space. We achieve state-of-the-art performance on the nuScenes autonomous driving benchmark.
  arXiv  Detail & Related papers  (2021-10-13T17:59:35Z)
• MonoCInIS: Camera Independent Monocular 3D Object Detection using Instance Segmentation [55.96577490779591]
  Methods need to have a degree of 'camera independence' in order to benefit from large and heterogeneous training data. We show that more data does not automatically guarantee a better performance, but rather, methods need to have a degree of 'camera independence' in order to benefit from large and heterogeneous training data.
  arXiv  Detail & Related papers  (2021-10-01T14:56:37Z)
• AutoShape: Real-Time Shape-Aware Monocular 3D Object Detection [15.244852122106634]
  We propose an approach for incorporating the shape-aware 2D/3D constraints into the 3D detection framework. Specifically, we employ the deep neural network to learn distinguished 2D keypoints in the 2D image domain. For generating the ground truth of 2D/3D keypoints, an automatic model-fitting approach has been proposed.
  arXiv  Detail & Related papers  (2021-08-25T08:50:06Z)
• FCOS3D: Fully Convolutional One-Stage Monocular 3D Object Detection [78.00922683083776]
  It is non-trivial to make a general adapted 2D detector work in this 3D task. In this technical report, we study this problem with a practice built on fully convolutional single-stage detector. Our solution achieves 1st place out of all the vision-only methods in the nuScenes 3D detection challenge of NeurIPS 2020.
  arXiv  Detail & Related papers  (2021-04-22T09:35:35Z)
• Relation3DMOT: Exploiting Deep Affinity for 3D Multi-Object Tracking from View Aggregation [8.854112907350624]
  3D multi-object tracking plays a vital role in autonomous navigation. Many approaches detect objects in 2D RGB sequences for tracking, which is lack of reliability when localizing objects in 3D space. We propose a novel convolutional operation, named RelationConv, to better exploit the correlation between each pair of objects in the adjacent frames.
  arXiv  Detail & Related papers  (2020-11-25T16:14:40Z)
• Learning 2D-3D Correspondences To Solve The Blind Perspective-n-Point Problem [98.92148855291363]
  This paper proposes a deep CNN model which simultaneously solves for both 6-DoF absolute camera pose 2D--3D correspondences. Tests on both real and simulated data have shown that our method substantially outperforms existing approaches.
  arXiv  Detail & Related papers  (2020-03-15T04:17:30Z)

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.