HV-BEV: Decoupling Horizontal and Vertical Feature Sampling for Multi-View 3D Object Detection

• URL: http://arxiv.org/abs/2412.18884v2
• Date: Mon, 30 Dec 2024 13:49:45 GMT
• Title: HV-BEV: Decoupling Horizontal and Vertical Feature Sampling for Multi-View 3D Object Detection
• Authors: Di Wu, Feng Yang, Benlian Xu, Pan Liao, Wenhui Zhao, Dingwen Zhang,
• Abstract summary: HV-BEV is a novel approach that decouples feature sampling in the BEV grid queries paradigm into horizontal feature aggregation and vertical adaptive height-aware reference point sampling. Our best-performing model achieves a remarkable 50.5% mAP and 59.8% NDS on the nuScenes testing set.
• Score: 34.72603963887331
• License:
• Abstract: The application of vision-based multi-view environmental perception system has been increasingly recognized in autonomous driving technology, especially the BEV-based models. Current state-of-the-art solutions primarily encode image features from each camera view into the BEV space through explicit or implicit depth prediction. However, these methods often focus on improving the accuracy of projecting 2D features into corresponding depth regions, while overlooking the highly structured information of real-world objects and the varying height distributions of objects across different scenes. In this work, we propose HV-BEV, a novel approach that decouples feature sampling in the BEV grid queries paradigm into horizontal feature aggregation and vertical adaptive height-aware reference point sampling, aiming to improve both the aggregation of objects' complete information and generalization to diverse road environments. Specifically, we construct a learnable graph structure in the horizontal plane aligned with the ground for 3D reference points, reinforcing the association of the same instance across different BEV grids, especially when the instance spans multiple image views around the vehicle. Additionally, instead of relying on uniform sampling within a fixed height range, we introduce a height-aware module that incorporates historical information, enabling the reference points to adaptively focus on the varying heights at which objects appear in different scenes. Extensive experiments validate the effectiveness of our proposed method, demonstrating its superior performance over the baseline across the nuScenes dataset. Moreover, our best-performing model achieves a remarkable 50.5% mAP and 59.8% NDS on the nuScenes testing set.

Related papers

• HeightLane: BEV Heightmap guided 3D Lane Detection [6.940660861207046]
  Accurate 3D lane detection from monocular images presents significant challenges due to depth ambiguity and imperfect ground modeling. Our study introduces HeightLane, an innovative method that predicts a height map from monocular images by creating anchors based on a multi-slope assumption. HeightLane achieves state-of-the-art performance in terms of F-score, highlighting its potential in real-world applications.
  arXiv  Detail & Related papers  (2024-08-15T17:14:57Z)
• OE-BevSeg: An Object Informed and Environment Aware Multimodal Framework for Bird's-eye-view Vehicle Semantic Segmentation [57.2213693781672]
  Bird's-eye-view (BEV) semantic segmentation is becoming crucial in autonomous driving systems. We propose OE-BevSeg, an end-to-end multimodal framework that enhances BEV segmentation performance. Our approach achieves state-of-the-art results by a large margin on the nuScenes dataset for vehicle segmentation.
  arXiv  Detail & Related papers  (2024-07-18T03:48:22Z)
• 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)
• 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)
• Towards Generalizable Multi-Camera 3D Object Detection via Perspective Debiasing [28.874014617259935]
  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.
  arXiv  Detail & Related papers  (2023-10-17T15:31:28Z)
• CoBEV: Elevating Roadside 3D Object Detection with Depth and Height Complementarity [34.025530326420146]
  We develop Complementary-BEV, a novel end-to-end monocular 3D object detection framework. We conduct extensive experiments on the public 3D detection benchmarks of roadside camera-based DAIR-V2X-I and Rope3D. For the first time, the vehicle AP score of a camera model reaches 80% on DAIR-V2X-I in terms of easy mode.
  arXiv  Detail & Related papers  (2023-10-04T13:38:53Z)
• OCBEV: Object-Centric BEV Transformer for Multi-View 3D Object Detection [29.530177591608297]
  Multi-view 3D object detection is becoming popular in autonomous driving due to its high effectiveness and low cost. Most of the current state-of-the-art detectors follow the query-based bird's-eye-view (BEV) paradigm. We propose an Object-Centric query-BEV detector OCBEV, which can carve the temporal and spatial cues of moving targets more effectively.
  arXiv  Detail & Related papers  (2023-06-02T17:59:48Z)
• 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)
• 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)
• 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)

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.