PolarBEVDet: Exploring Polar Representation for Multi-View 3D Object Detection in Bird's-Eye-View

• URL: http://arxiv.org/abs/2408.16200v1
• Date: Thu, 29 Aug 2024 01:42:38 GMT
• Title: PolarBEVDet: Exploring Polar Representation for Multi-View 3D Object Detection in Bird's-Eye-View
• Authors: Zichen Yu, Quanli Liu, Wei Wang, Liyong Zhang, Xiaoguang Zhao,
• Abstract summary: We propose to employ the polar BEV representation to substitute the Cartesian BEV representation. Experiments on nuScenes show that PolarBEVDet achieves the superior performance.
• Score: 5.0458717114406975
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, LSS-based multi-view 3D object detection provides an economical and deployment-friendly solution for autonomous driving. However, all the existing LSS-based methods transform multi-view image features into a Cartesian Bird's-Eye-View(BEV) representation, which does not take into account the non-uniform image information distribution and hardly exploits the view symmetry. In this paper, in order to adapt the image information distribution and preserve the view symmetry by regular convolution, we propose to employ the polar BEV representation to substitute the Cartesian BEV representation. To achieve this, we elaborately tailor three modules: a polar view transformer to generate the polar BEV representation, a polar temporal fusion module for fusing historical polar BEV features and a polar detection head to predict the polar-parameterized representation of the object. In addition, we design a 2D auxiliary detection head and a spatial attention enhancement module to improve the quality of feature extraction in perspective view and BEV, respectively. Finally, we integrate the above improvements into a novel multi-view 3D object detector, PolarBEVDet. Experiments on nuScenes show that PolarBEVDet achieves the superior performance. The code is available at https://github.com/Yzichen/PolarBEVDet.git.

Related papers

• VFMM3D: Releasing the Potential of Image by Vision Foundation Model for Monocular 3D Object Detection [80.62052650370416]
  monocular 3D object detection holds significant importance across various applications, including autonomous driving and robotics. In this paper, we present VFMM3D, an innovative framework that leverages the capabilities of Vision Foundation Models (VFMs) to accurately transform single-view images into LiDAR point cloud representations.
  arXiv  Detail & Related papers  (2024-04-15T03:12:12Z)
• BEV-IO: Enhancing Bird's-Eye-View 3D Detection with Instance Occupancy [58.92659367605442]
  We present BEV-IO, a new 3D detection paradigm to enhance BEV representation with instance occupancy information. We show that BEV-IO can outperform state-of-the-art methods while only adding a negligible increase in parameters and computational overhead.
  arXiv  Detail & Related papers  (2023-05-26T11:16:12Z)
• Leveraging BEV Representation for 360-degree Visual Place Recognition [14.497501941931759]
  This paper investigates the advantages of using Bird's Eye View representation in 360-degree visual place recognition (VPR) We propose a novel network architecture that utilizes the BEV representation in feature extraction, feature aggregation, and vision-LiDAR fusion. The proposed BEV-based method is evaluated in ablation and comparative studies on two datasets.
  arXiv  Detail & Related papers  (2023-05-23T08:29:42Z)
• OA-BEV: Bringing Object Awareness to Bird's-Eye-View Representation for Multi-Camera 3D Object Detection [78.38062015443195]
  OA-BEV is a network that can be plugged into the BEV-based 3D object detection framework. Our method achieves consistent improvements over the BEV-based baselines in terms of both average precision and nuScenes detection score.
  arXiv  Detail & Related papers  (2023-01-13T06:02:31Z)
• Multi-Projection Fusion and Refinement Network for Salient Object Detection in 360{\deg} Omnidirectional Image [141.10227079090419]
  We propose a Multi-Projection Fusion and Refinement Network (MPFR-Net) to detect the salient objects in 360deg omnidirectional image. MPFR-Net uses the equirectangular projection image and four corresponding cube-unfolding images as inputs. Experimental results on two omnidirectional datasets demonstrate that the proposed approach outperforms the state-of-the-art methods both qualitatively and quantitatively.
  arXiv  Detail & Related papers  (2022-12-23T14:50:40Z)
• Vision-based Uneven BEV Representation Learning with Polar Rasterization and Surface Estimation [42.071461405587264]
  We propose PolarBEV for vision-based uneven BEV representation learning. PolarBEV keeps real-time inference speed on a single 2080Ti GPU.
  arXiv  Detail & Related papers  (2022-07-05T08:20:36Z)
• PolarFormer: Multi-camera 3D Object Detection with Polar Transformers [93.49713023975727]
  3D object detection in autonomous driving aims to reason "what" and "where" the objects of interest present in a 3D world. Existing methods often adopt the canonical Cartesian coordinate system with perpendicular axis. We propose a new Polar Transformer (PolarFormer) for more accurate 3D object detection in the bird's-eye-view (BEV) taking as input only multi-camera 2D images.
  arXiv  Detail & Related papers  (2022-06-30T16:32:48Z)
• Polar Parametrization for Vision-based Surround-View 3D Detection [35.2870826850481]
  Polar Parametrization for 3D detection reformulates position parametrization, velocity decomposition, perception range, label assignment and loss function. Based on Polar Parametrization, we propose surround-view 3D DEtection TRansformer, named PolarDETR.
  arXiv  Detail & Related papers  (2022-06-22T10:26:12Z)
• SGM3D: Stereo Guided Monocular 3D Object Detection [62.11858392862551]
  We propose a stereo-guided monocular 3D object detection network, termed SGM3D. We exploit robust 3D features extracted from stereo images to enhance the features learned from the monocular image. Our method can be integrated into many other monocular approaches to boost performance without introducing any extra computational cost.
  arXiv  Detail & Related papers  (2021-12-03T13:57:14Z)

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.