Sparse BEV Fusion with Self-View Consistency for Multi-View Detection and Tracking

• URL: http://arxiv.org/abs/2509.08421v1
• Date: Wed, 10 Sep 2025 09:06:41 GMT
• Title: Sparse BEV Fusion with Self-View Consistency for Multi-View Detection and Tracking
• Authors: Keisuke Toida, Taigo Sakai, Naoki Kato, Kazutoyo Yokota, Takeshi Nakamura, Kazuhiro Hotta,
• Abstract summary: We propose SCFusion, a framework that combines three techniques to improve multi-view feature integration.<n> SCFusion achieves state-of-the-art performance, reaching an IDF1 score of 95.9% on WildTrack and a MODP of 89.2% on MultiviewX.
• Score: 15.680801582969393
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multi-View Multi-Object Tracking (MVMOT) is essential for applications such as surveillance, autonomous driving, and sports analytics. However, maintaining consistent object identities across multiple cameras remains challenging due to viewpoint changes, lighting variations, and occlusions, which often lead to tracking errors.Recent methods project features from multiple cameras into a unified Bird's-Eye-View (BEV) space to improve robustness against occlusion. However, this projection introduces feature distortion and non-uniform density caused by variations in object scale with distance. These issues degrade the quality of the fused representation and reduce detection and tracking accuracy.To address these problems, we propose SCFusion, a framework that combines three techniques to improve multi-view feature integration. First, it applies a sparse transformation to avoid unnatural interpolation during projection. Next, it performs density-aware weighting to adaptively fuse features based on spatial confidence and camera distance. Finally, it introduces a multi-view consistency loss that encourages each camera to learn discriminative features independently before fusion.Experiments show that SCFusion achieves state-of-the-art performance, reaching an IDF1 score of 95.9% on WildTrack and a MODP of 89.2% on MultiviewX, outperforming the baseline method TrackTacular. These results demonstrate that SCFusion effectively mitigates the limitations of conventional BEV projection and provides a robust and accurate solution for multi-view object detection and tracking.

Related papers

• DINO-CoDT: Multi-class Collaborative Detection and Tracking with Vision Foundation Models [11.34839442803445]
  We propose a multi-class collaborative detection and tracking framework tailored for diverse road users.<n>We first present a detector with a global spatial attention fusion (GSAF) module, enhancing multi-scale feature learning for objects of varying sizes.<n>Next, we introduce a tracklet RE-IDentification (REID) module that leverages visual semantics with a vision foundation model to effectively reduce ID SWitch (IDSW) errors.
  arXiv  Detail & Related papers  (2025-06-09T02:49:10Z)
• A Global Depth-Range-Free Multi-View Stereo Transformer Network with Pose Embedding [76.44979557843367]
  We propose a novel multi-view stereo (MVS) framework that gets rid of the depth range prior.<n>We introduce a Multi-view Disparity Attention (MDA) module to aggregate long-range context information.<n>We explicitly estimate the quality of the current pixel corresponding to sampled points on the epipolar line of the source image.
  arXiv  Detail & Related papers  (2024-11-04T08:50:16Z)
• Progressive Multi-Modal Fusion for Robust 3D Object Detection [12.048303829428452]
  Existing methods perform sensor fusion in a single view by projecting features from both modalities either in Bird's Eye View (BEV) or Perspective View (PV) We propose ProFusion3D, a progressive fusion framework that combines features in both BEV and PV at both intermediate and object query levels. Our architecture hierarchically fuses local and global features, enhancing the robustness of 3D object detection.
  arXiv  Detail & Related papers  (2024-10-09T22:57:47Z)
• DVPE: Divided View Position Embedding for Multi-View 3D Object Detection [7.791229698270439]
  Current research faces challenges in balancing between receptive fields and reducing interference when aggregating multi-view features. This paper proposes a divided view method, in which features are modeled globally via the visibility crossattention mechanism, but interact only with partial features in a divided local virtual space. Our framework, named DVPE, achieves state-of-the-art performance (57.2% mAP and 64.5% NDS) on the nuScenes test set.
  arXiv  Detail & Related papers  (2024-07-24T02:44:41Z)
• 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)
• MSMDFusion: Fusing LiDAR and Camera at Multiple Scales with Multi-Depth Seeds for 3D Object Detection [89.26380781863665]
  Fusing LiDAR and camera information is essential for achieving accurate and reliable 3D object detection in autonomous driving systems. Recent approaches aim at exploring the semantic densities of camera features through lifting points in 2D camera images into 3D space for fusion. We propose a novel framework that focuses on the multi-scale progressive interaction of the multi-granularity LiDAR and camera features.
  arXiv  Detail & Related papers  (2022-09-07T12:29:29Z)
• CAMO-MOT: Combined Appearance-Motion Optimization for 3D Multi-Object Tracking with Camera-LiDAR Fusion [34.42289908350286]
  3D Multi-object tracking (MOT) ensures consistency during continuous dynamic detection. It can be challenging to accurately track the irregular motion of objects for LiDAR-based methods. We propose a novel camera-LiDAR fusion 3D MOT framework based on the Combined Appearance-Motion Optimization (CAMO-MOT)
  arXiv  Detail & Related papers  (2022-09-06T14:41:38Z)
• BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation [105.96557764248846]
  We introduce BEVFusion, a generic multi-task multi-sensor fusion framework. It unifies multi-modal features in the shared bird's-eye view representation space. It achieves 1.3% higher mAP and NDS on 3D object detection and 13.6% higher mIoU on BEV map segmentation, with 1.9x lower cost.
  arXiv  Detail & Related papers  (2022-05-26T17:59:35Z)
• SurroundDepth: Entangling Surrounding Views for Self-Supervised Multi-Camera Depth Estimation [101.55622133406446]
  We propose a SurroundDepth method to incorporate the information from multiple surrounding views to predict depth maps across cameras. Specifically, we employ a joint network to process all the surrounding views and propose a cross-view transformer to effectively fuse the information from multiple views. In experiments, our method achieves the state-of-the-art performance on the challenging multi-camera depth estimation datasets.
  arXiv  Detail & Related papers  (2022-04-07T17:58:47Z)
• Interactive Multi-scale Fusion of 2D and 3D Features for Multi-object Tracking [23.130490413184596]
  We introduce PointNet++ to obtain multi-scale deep representations of point cloud to make it adaptive to our proposed Interactive Feature Fusion. Our method can achieve good performance on the KITTI benchmark and outperform other approaches without using multi-scale feature fusion.
  arXiv  Detail & Related papers  (2022-03-30T13:00:27Z)
• DeepFusion: Lidar-Camera Deep Fusion for Multi-Modal 3D Object Detection [83.18142309597984]
  Lidars and cameras are critical sensors that provide complementary information for 3D detection in autonomous driving. We develop a family of generic multi-modal 3D detection models named DeepFusion, which is more accurate than previous methods.
  arXiv  Detail & Related papers  (2022-03-15T18:46:06Z)
• Hybrid Tracker with Pixel and Instance for Video Panoptic Segmentation [50.62685357414904]
  Video Panoptic coefficient (VPS) aims to generate coherent panoptic segmentation and track the identities of all pixels across video frames. We present HybridTracker, a lightweight and joint tracking model attempting to eliminate the limitations of the single tracker. Comprehensive experiments show that HybridTracker achieves superior performance than state-of-the-art methods on Cityscapes-VPS and VIPER datasets.
  arXiv  Detail & Related papers  (2022-03-02T16:21:55Z)

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.