title: RoCo-Sim: Enhancing Roadside Collaborative Perception through Foreground
    Simulation
summary: Roadside Collaborative Perception refers to a system where multiple roadside
  units collaborate to pool their perceptual data, assisting vehicles in
  enhancing their environmental awareness. Existing roadside perception methods
  concentrate on model design but overlook data issues like calibration errors,
  sparse information, and multi-view consistency, leading to poor performance on
  recent published datasets. To significantly enhance roadside collaborative
  perception and address critical data issues, we present the first simulation
  framework RoCo-Sim for road-side collaborative perception. RoCo-Sim is capable
  of generating diverse, multi-view consistent simulated roadside data through
  dynamic foreground editing and full-scene style transfer of a single image.
  RoCo-Sim consists of four components: (1) Camera Extrinsic Optimization ensures
  accurate 3D to 2D projection for roadside cameras; (2) A novel Multi-View
  Occlusion-Aware Sampler (MOAS) determines the placement of diverse digital
  assets within 3D space; (3) DepthSAM innovatively models foreground-background
  relationships from single-frame fixed-view images, ensuring multi-view
  consistency of foreground; and (4) Scalable Post-Processing Toolkit generates
  more realistic and enriched scenes through style transfer and other
  enhancements. RoCo-Sim significantly improves roadside 3D object detection,
  outperforming SOTA methods by 83.74 on Rcooper-Intersection and 83.12 on
  TUMTraf-V2X for AP70. RoCo-Sim fills a critical gap in roadside perception
  simulation. Code and pre-trained models will be released soon:
  https://github.com/duyuwen-duen/RoCo-Sim
paper_authors._id: 67d9638d92e48ed07860ecee
paper_authors.name: Yuwen Du
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paper_authors.name: Anning Hu
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paper_authors.fullname: Anning Hu
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paper_authors.user: PathfinderToT
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paper_authors.name: Zichen Chao
paper_authors._id: 67d9638d92e48ed07860ecf1
paper_authors.name: Yifan Lu
paper_authors.status: admin_assigned
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paper_authors.fullname: Yifan Lu
paper_authors.type: user
paper_authors.user: yifanlu
paper_authors._id: 67d9638d92e48ed07860ecf2
paper_authors.name: Junhao Ge
paper_authors.status: admin_assigned
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paper_authors._id: 664aebb7cf5fd472d7c37b2f
paper_authors.avatarUrl: /avatars/59b44b608a27269ab680cafa29a50f18.svg
paper_authors.fullname: Junhao Ge
paper_authors.type: user
paper_authors.user: Cancaries
paper_authors._id: 67d9638d92e48ed07860ecf3
paper_authors.name: Genjia Liu
paper_authors.status: admin_assigned
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paper_authors._id: 634187b38d8089ebaef3dc18
paper_authors.avatarUrl: /avatars/a225077ee00d34cd4c084e3e347ce047.svg
paper_authors.fullname: Genjia Liu
paper_authors.type: user
paper_authors.user: gjliu
paper_authors._id: 67d9638d92e48ed07860ecf4
paper_authors.name: Weitao Wu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-19T13:45:39.488Z
paper_authors._id: 65bc8587262a04f94c38808f
paper_authors.avatarUrl: /avatars/9478bb68e931edc9b0eba86807de5579.svg
paper_authors.fullname: Wuweitao
paper_authors.type: user
paper_authors.user: VictorWuweitao
paper_authors._id: 67d9638d92e48ed07860ecf5
paper_authors.name: Lanjun Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-19T13:45:46.738Z
paper_authors._id: 646ec1c65d05113e4327d900
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paper_authors.fullname: Lanjun Wang
paper_authors.type: user
paper_authors.user: lanzerwang
paper_authors._id: 67d9638d92e48ed07860ecf6
paper_authors.name: Siheng Chen
paper_authors.status: admin_assigned
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paper_authors.avatarUrl: /avatars/9bffd3fb567d2fa1e5c3546d77560b43.svg
paper_authors.fullname: Siheng Chen
paper_authors.type: user
paper_authors.user: sihengchen
paper_title: RoCo-Sim: Enhancing Roadside Collaborative Perception through Foreground
    Simulation
paper_summary: Roadside Collaborative Perception refers to a system where multiple roadside
  units collaborate to pool their perceptual data, assisting vehicles in
  enhancing their environmental awareness. Existing roadside perception methods
  concentrate on model design but overlook data issues like calibration errors,
  sparse information, and multi-view consistency, leading to poor performance on
  recent published datasets. To significantly enhance roadside collaborative
  perception and address critical data issues, we present the first simulation
  framework RoCo-Sim for road-side collaborative perception. RoCo-Sim is capable
  of generating diverse, multi-view consistent simulated roadside data through
  dynamic foreground editing and full-scene style transfer of a single image.
  RoCo-Sim consists of four components: (1) Camera Extrinsic Optimization ensures
  accurate 3D to 2D projection for roadside cameras; (2) A novel Multi-View
  Occlusion-Aware Sampler (MOAS) determines the placement of diverse digital
  assets within 3D space; (3) DepthSAM innovatively models foreground-background
  relationships from single-frame fixed-view images, ensuring multi-view
  consistency of foreground; and (4) Scalable Post-Processing Toolkit generates
  more realistic and enriched scenes through style transfer and other
  enhancements. RoCo-Sim significantly improves roadside 3D object detection,
  outperforming SOTA methods by 83.74 on Rcooper-Intersection and 83.12 on
  TUMTraf-V2X for AP70. RoCo-Sim fills a critical gap in roadside perception
  simulation. Code and pre-trained models will be released soon:
  https://github.com/duyuwen-duen/RoCo-Sim
paper_ai_summary: RoCo-Sim is a simulation framework for roadside collaborative perception that generates diverse, multi-view consistent data to improve object detection performance.
paper_ai_keywords: Camera Extrinsic Optimization Multi-View Occlusion-Aware Sampler DepthSAM Scalable Post-Processing Toolkit 3D object detection AP70 Rcooper-Intersection TUMTraf-V2X