TopoBDA: Towards Bezier Deformable Attention for Road Topology Understanding

• URL: http://arxiv.org/abs/2412.18951v1
• Date: Wed, 25 Dec 2024 17:31:54 GMT
• Title: TopoBDA: Towards Bezier Deformable Attention for Road Topology Understanding
• Authors: Muhammet Esat Kalfaoglu, Halil Ibrahim Ozturk, Ozsel Kilinc, Alptekin Temizel,
• Abstract summary: TopoBDA (Topology with Bezier Deformable Attention) is a novel approach that enhances road topology understanding.<n>BDA utilizes Bezier control points to drive the deformable attention mechanism.<n>TopoBDA processes multi-camera 360-degree imagery to generate Bird's Eye View (BEV) features, which are refined through a transformer decoder employing BDA.
• Score: 2.8498944632323755
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Understanding road topology is crucial for autonomous driving. This paper introduces TopoBDA (Topology with Bezier Deformable Attention), a novel approach that enhances road topology understanding by leveraging Bezier Deformable Attention (BDA). BDA utilizes Bezier control points to drive the deformable attention mechanism, significantly improving the detection and representation of elongated and thin polyline structures, such as lane centerlines. TopoBDA processes multi-camera 360-degree imagery to generate Bird's Eye View (BEV) features, which are refined through a transformer decoder employing BDA. This method enhances computational efficiency while maintaining high accuracy in centerline prediction. Additionally, TopoBDA incorporates an instance mask formulation and an auxiliary one-to-many set prediction loss strategy to further refine centerline detection and improve road topology understanding. Experimental evaluations on the OpenLane-V2 dataset demonstrate that TopoBDA outperforms existing methods, achieving state-of-the-art results in centerline detection and topology reasoning. The integration of multi-modal data, including lidar and radar, specifically for road topology understanding, further enhances the model's performance, underscoring its importance in autonomous driving applications.

Related papers

• DB3D-L: Depth-aware BEV Feature Transformation for Accurate 3D Lane Detection [3.7115000857388494]
  3D Lane detection plays an important role in autonomous driving.<n>Recent advances primarily build Birds-Eye-View (BEV) feature from front-view (FV) images to perceive 3D information of Lane more effectively.<n>However, constructing accurate BEV information from FV image is limited due to the lacking of depth information.
  arXiv  Detail & Related papers  (2025-05-19T15:47:20Z)
• Depth3DLane: Monocular 3D Lane Detection via Depth Prior Distillation [5.909083729156255]
  We introduce a BEV-based framework to address limitations and improve 3D lane detection accuracy. We leverage Depth Prior Distillation to transfer semantic depth knowledge from a teacher model. Our method achieves state-of-the-art performance in terms of z-axis error.
  arXiv  Detail & Related papers  (2025-04-25T13:08:41Z)
• RaCFormer: Towards High-Quality 3D Object Detection via Query-based Radar-Camera Fusion [58.77329237533034]
  We propose a Radar-Camera fusion transformer (RaCFormer) to boost the accuracy of 3D object detection. RaCFormer achieves superior results of 64.9% mAP and 70.2% on nuScenes datasets.
  arXiv  Detail & Related papers  (2024-12-17T09:47:48Z)
• TopoSD: Topology-Enhanced Lane Segment Perception with SDMap Prior [70.84644266024571]
  We propose to train a perception model to "see" standard definition maps (SDMaps) We encode SDMap elements into neural spatial map representations and instance tokens, and then incorporate such complementary features as prior information. Based on the lane segment representation framework, the model simultaneously predicts lanes, centrelines and their topology.
  arXiv  Detail & Related papers  (2024-11-22T06:13:42Z)
• DV-3DLane: End-to-end Multi-modal 3D Lane Detection with Dual-view Representation [40.71071200694655]
  We present DV-3DLane, a novel end-to-end Dual-View multi-modal 3D Lane detection framework. It synergizes the strengths of both images and LiDAR points. It achieves state-of-the-art performance, with a remarkable 11.2 gain in F1 score and a substantial 53.5% reduction in errors.
  arXiv  Detail & Related papers  (2024-06-23T10:48:42Z)
• 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)
• FENet: Focusing Enhanced Network for Lane Detection [0.0]
  This research pioneers networks augmented with Focusing Sampling, Partial Field of View Evaluation, Enhanced FPN architecture and Directional IoU Loss. Experiments demonstrate our Focusing Sampling strategy, emphasizing vital distant details unlike uniform approaches. Future directions include collecting on-road data and integrating complementary dual frameworks to further breakthroughs guided by human perception principles.
  arXiv  Detail & Related papers  (2023-12-28T17:52:09Z)
• RadOcc: Learning Cross-Modality Occupancy Knowledge through Rendering Assisted Distillation [50.35403070279804]
  3D occupancy prediction is an emerging task that aims to estimate the occupancy states and semantics of 3D scenes using multi-view images. We propose RadOcc, a Rendering assisted distillation paradigm for 3D Occupancy prediction.
  arXiv  Detail & Related papers  (2023-12-19T03:39:56Z)
• Instance-aware Multi-Camera 3D Object Detection with Structural Priors Mining and Self-Boosting Learning [93.71280187657831]
  Camera-based bird-eye-view (BEV) perception paradigm has made significant progress in the autonomous driving field. We propose IA-BEV, which integrates image-plane instance awareness into the depth estimation process within a BEV-based detector.
  arXiv  Detail & Related papers  (2023-12-13T09:24:42Z)
• ODM3D: Alleviating Foreground Sparsity for Semi-Supervised Monocular 3D Object Detection [15.204935788297226]
  ODM3D framework entails cross-modal knowledge distillation at various levels to inject LiDAR-domain knowledge into a monocular detector during training. By identifying foreground sparsity as the main culprit behind existing methods' suboptimal training, we exploit the precise localisation information embedded in LiDAR points. Our method ranks 1st in both KITTI validation and test benchmarks, significantly surpassing all existing monocular methods, supervised or semi-supervised.
  arXiv  Detail & Related papers  (2023-10-28T07:12:09Z)
• Improving Online Lane Graph Extraction by Object-Lane Clustering [106.71926896061686]
  We propose an architecture and loss formulation to improve the accuracy of local lane graph estimates. The proposed method learns to assign the objects to centerlines by considering the centerlines as cluster centers. We show that our method can achieve significant performance improvements by using the outputs of existing 3D object detection methods.
  arXiv  Detail & Related papers  (2023-07-20T15:21:28Z)
• An Efficient Transformer for Simultaneous Learning of BEV and Lane Representations in 3D Lane Detection [55.281369497158515]
  We propose an efficient transformer for 3D lane detection. Different from the vanilla transformer, our model contains a cross-attention mechanism to simultaneously learn lane and BEV representations. Our method obtains 2D and 3D lane predictions by applying the lane features to the image-view and BEV features, respectively.
  arXiv  Detail & Related papers  (2023-06-08T04:18:31Z)
• 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)
• Dense Voxel Fusion for 3D Object Detection [10.717415797194896]
  Voxel Fusion (DVF) is a sequential fusion method that generates multi-scale dense voxel feature representations. We train directly with ground truth 2D bounding box labels, avoiding noisy, detector-specific, 2D predictions. We show that our proposed multi-modal training strategy results in better generalization compared to training using erroneous 2D predictions.
  arXiv  Detail & Related papers  (2022-03-02T04:51:31Z)

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.