Towards Generalizable Multi-Camera 3D Object Detection via Perspective Debiasing

• URL: http://arxiv.org/abs/2310.11346v3
• Date: Mon, 25 Dec 2023 16:30:00 GMT
• Title: Towards Generalizable Multi-Camera 3D Object Detection via Perspective Debiasing
• Authors: Hao Lu, Yunpeng Zhang, Qing Lian, Dalong Du, Yingcong Chen
• Abstract summary: Multi-Camera 3D Object Detection (MC3D-Det) has gained prominence with the advent of bird's-eye view (BEV) approaches. We propose a novel method that aligns 3D detection with 2D camera plane results, ensuring consistent and accurate detections.
• Score: 28.874014617259935
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Detecting objects in 3D space using multiple cameras, known as Multi-Camera 3D Object Detection (MC3D-Det), has gained prominence with the advent of bird's-eye view (BEV) approaches. However, these methods often struggle when faced with unfamiliar testing environments due to the lack of diverse training data encompassing various viewpoints and environments. To address this, we propose a novel method that aligns 3D detection with 2D camera plane results, ensuring consistent and accurate detections. Our framework, anchored in perspective debiasing, helps the learning of features resilient to domain shifts. In our approach, we render diverse view maps from BEV features and rectify the perspective bias of these maps, leveraging implicit foreground volumes to bridge the camera and BEV planes. This two-step process promotes the learning of perspective- and context-independent features, crucial for accurate object detection across varying viewpoints, camera parameters, and environmental conditions. Notably, our model-agnostic approach preserves the original network structure without incurring additional inference costs, facilitating seamless integration across various models and simplifying deployment. Furthermore, we also show our approach achieves satisfactory results in real data when trained only with virtual datasets, eliminating the need for real scene annotations. Experimental results on both Domain Generalization (DG) and Unsupervised Domain Adaptation (UDA) clearly demonstrate its effectiveness. The codes are available at https://github.com/EnVision-Research/Generalizable-BEV.

Related papers

• Towards Unified 3D Object Detection via Algorithm and Data Unification [70.27631528933482]
  We build the first unified multi-modal 3D object detection benchmark MM- Omni3D and extend the aforementioned monocular detector to its multi-modal version. We name the designed monocular and multi-modal detectors as UniMODE and MM-UniMODE, respectively.
  arXiv  Detail & Related papers  (2024-02-28T18:59:31Z)
• VirtualPainting: Addressing Sparsity with Virtual Points and Distance-Aware Data Augmentation for 3D Object Detection [3.5259183508202976]
  We present an innovative approach that involves the generation of virtual LiDAR points using camera images. We also enhance these virtual points with semantic labels obtained from image-based segmentation networks. Our approach offers a versatile solution that can be seamlessly integrated into various 3D frameworks and 2D semantic segmentation methods.
  arXiv  Detail & Related papers  (2023-12-26T18:03:05Z)
• SOGDet: Semantic-Occupancy Guided Multi-view 3D Object Detection [19.75965521357068]
  We propose a novel approach called SOGDet (Semantic-Occupancy Guided Multi-view 3D Object Detection) to improve the accuracy of 3D object detection. Our results show that SOGDet consistently enhance the performance of three baseline methods in terms of nuScenes Detection Score (NDS) and mean Average Precision (mAP) This indicates that the combination of 3D object detection and 3D semantic occupancy leads to a more comprehensive perception of the 3D environment, thereby aiding build more robust autonomous driving systems.
  arXiv  Detail & Related papers  (2023-08-26T07:38:21Z)
• Geometric-aware Pretraining for Vision-centric 3D Object Detection [77.7979088689944]
  We propose a novel geometric-aware pretraining framework called GAPretrain. GAPretrain serves as a plug-and-play solution that can be flexibly applied to multiple state-of-the-art detectors. We achieve 46.2 mAP and 55.5 NDS on the nuScenes val set using the BEVFormer method, with a gain of 2.7 and 2.1 points, respectively.
  arXiv  Detail & Related papers  (2023-04-06T14:33:05Z)
• Towards Domain Generalization for Multi-view 3D Object Detection in Bird-Eye-View [11.958753088613637]
  We first analyze the causes of the domain gap for the MV3D-Det task. To acquire a robust depth prediction, we propose to decouple the depth estimation from intrinsic parameters of the camera. We modify the focal length values to create multiple pseudo-domains and construct an adversarial training loss to encourage the feature representation to be more domain-agnostic.
  arXiv  Detail & Related papers  (2023-03-03T02:59:13Z)
• 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)
• 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)
• Towards Model Generalization for Monocular 3D Object Detection [57.25828870799331]
  We present an effective unified camera-generalized paradigm (CGP) for Mono3D object detection. We also propose the 2D-3D geometry-consistent object scaling strategy (GCOS) to bridge the gap via an instance-level augment. Our method called DGMono3D achieves remarkable performance on all evaluated datasets and surpasses the SoTA unsupervised domain adaptation scheme.
  arXiv  Detail & Related papers  (2022-05-23T23:05:07Z)
• BirdNet+: End-to-End 3D Object Detection in LiDAR Bird's Eye View [117.44028458220427]
  On-board 3D object detection in autonomous vehicles often relies on geometry information captured by LiDAR devices. We present a fully end-to-end 3D object detection framework that can infer oriented 3D boxes solely from BEV images.
  arXiv  Detail & Related papers  (2020-03-09T15:08:40Z)

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.