ViCo3D: Empowering LiDAR-based Collaborative 3D Object Detection with Vision Foundation Models

• URL: http://arxiv.org/abs/2607.12959v1
• Date: Tue, 14 Jul 2026 16:56:10 GMT
• Title: ViCo3D: Empowering LiDAR-based Collaborative 3D Object Detection with Vision Foundation Models
• Abstract summary: ViCo3D is a collaborative 3D object detection framework powered by vision foundation models (VFMs)<n>It adapts VFMs to LiDAR-based collaborative perception from three aspects.<n>Experiments on DAIR-V2X and V2XSet demonstrate that ViCo3D achieves state-of-the-art 3D detection performance.
• Score: 38.539942506000166
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: LiDAR-based collaborative 3D perception in Vehicle-to-Everything (V2X) systems typically relies on fusing bird's-eye-view (BEV) features across agents. However, current BEV representations, typically extracted by LiDAR backbones trained from scratch, are geometry-dominated and lack general semantic priors, inherently limiting the efficacy of feature-level collaboration. Meanwhile, vision foundation models (VFMs) pretrained on large-scale image data have demonstrated strong capability in learning general-purpose and informative visual representations for 2D tasks, and have the potential to enhance agent-wise LiDAR BEV representations for collaboration. Despite this potential, adapting VFMs to LiDAR-based 3D detection remains challenging due to the substantial image-point cloud modality gap. To bridge this gap, we propose ViCo3D, a collaborative 3D object detection framework powered by VFMs. Specifically, ViCo3D adapts VFMs to LiDAR-based collaborative perception from three aspects: First, ViCo3D projects point clouds onto the BEV plane as three-channel images, enabling DINOv2 to extract BEV-space visual features from LiDAR inputs. Besides, to effectively integrate these DINOv2-derived features with LiDAR geometric features, ViCo3D introduces a multi-scale BEV fusion module within the single-agent encoder. In addition, ViCo3D adopts an ego-centric cross-agent fusion strategy to aggregate complementary information from multiple agents. Experiments on DAIR-V2X and V2XSet demonstrate that ViCo3D achieves state-of-the-art 3D detection performance. Remarkably, it delivers up to 1.8x greater collaborative gains than prior methods on DAIR-V2X. The code will be made public available for future investigation.

Related papers

• CoGoal3D: Collaborative 3D Object Detection with 3D-Aware Fusion and Refinement [26.562858293023414]
  We propose a novel collaborative 3D object detection framework called CoGoal3D.<n>CoGoal3D extracts and refines the 3D feature gradually in a two-stage pipeline.<n>Experiments on public real-world datasets demonstrate that our framework achieves new state-of-the-art performance.
  arXiv  Detail & Related papers  (2026-07-21T12:20:48Z)
• AugVLA-3D: Depth-Driven Feature Augmentation for Vision-Language-Action Models [42.57469056850227]
  Vision-Language-Action (VLA) models have recently achieved remarkable progress in robotic perception and control.<n>We propose a novel framework that integrates depth estimation into VLA models to enrich 3D feature representations.
  arXiv  Detail & Related papers  (2026-02-11T09:57:32Z)
• TriCLIP-3D: A Unified Parameter-Efficient Framework for Tri-Modal 3D Visual Grounding based on CLIP [52.79100775328595]
  3D visual grounding allows an embodied agent to understand visual information in real-world 3D environments based on human instructions.<n>Existing 3D visual grounding methods rely on separate encoders for different modalities.<n>We propose a unified 2D pre-trained multi-modal network to process all three modalities.
  arXiv  Detail & Related papers  (2025-07-20T10:28:06Z)
• Label-Efficient LiDAR Semantic Segmentation with 2D-3D Vision Transformer Adapters [32.21090169762889]
  BALViT is a novel approach that leverages frozen vision models as amodal feature encoders for learning strong LiDAR encoders.<n>We make the code and models publicly available at: http://balvit.cs.uni-freiburg.de.
  arXiv  Detail & Related papers  (2025-03-05T09:30:49Z)
• LargeAD: Large-Scale Cross-Sensor Data Pretraining for Autonomous Driving [88.85002707211777]
  LargeAD is a versatile and scalable framework designed for large-scale 3D pretraining across diverse real-world driving datasets.<n>Our framework leverages VFMs to extract semantically rich superpixels from 2D images, which are aligned with LiDAR point clouds.<n>This alignment facilitates cross-modal representation learning, enhancing the semantic consistency between 2D and 3D data.
  arXiv  Detail & Related papers  (2025-01-07T18:59:59Z)
• 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)
• CooPre: Cooperative Pretraining for V2X Cooperative Perception [47.00472259100765]
  CooPre is a self-supervised learning framwork for V2X cooperative perception.<n>We develop a V2X bird-eye-view (BEV) guided masking strategy which effectively allows the model to pay attention to 3D features across heterogeneous V2X agents.<n>CooPre achieves a 4% mAP improvement on V2X-Real dataset and surpasses baseline performance using only 50% of the training data.
  arXiv  Detail & Related papers  (2024-08-20T23:39:26Z)
• 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)
• LiDAR-Based 3D Object Detection via Hybrid 2D Semantic Scene Generation [38.38852904444365]
  This paper proposes a novel scene representation that encodes both the semantics and geometry of the 3D environment in 2D. Our simple yet effective design can be easily integrated into most state-of-the-art 3D object detectors.
  arXiv  Detail & Related papers  (2023-04-04T04:05:56Z)
• Fully Convolutional One-Stage 3D Object Detection on LiDAR Range Images [96.66271207089096]
  FCOS-LiDAR is a fully convolutional one-stage 3D object detector for LiDAR point clouds of autonomous driving scenes. We show that an RV-based 3D detector with standard 2D convolutions alone can achieve comparable performance to state-of-the-art BEV-based detectors.
  arXiv  Detail & Related papers  (2022-05-27T05:42:16Z)
• M^2BEV: Multi-Camera Joint 3D Detection and Segmentation with Unified Birds-Eye View Representation [145.6041893646006]
  M$2$BEV is a unified framework that jointly performs 3D object detection and map segmentation. M$2$BEV infers both tasks with a unified model and improves efficiency.
  arXiv  Detail & Related papers  (2022-04-11T13:43:25Z)

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.