title: MonoPlace3D: Learning 3D-Aware Object Placement for 3D Monocular
    Detection
summary: Current monocular 3D detectors are held back by the limited diversity and
  scale of real-world datasets. While data augmentation certainly helps, it's
  particularly difficult to generate realistic scene-aware augmented data for
  outdoor settings. Most current approaches to synthetic data generation focus on
  realistic object appearance through improved rendering techniques. However, we
  show that where and how objects are positioned is just as crucial for training
  effective 3D monocular detectors. The key obstacle lies in automatically
  determining realistic object placement parameters - including position,
  dimensions, and directional alignment when introducing synthetic objects into
  actual scenes. To address this, we introduce MonoPlace3D, a novel system that
  considers the 3D scene content to create realistic augmentations. Specifically,
  given a background scene, MonoPlace3D learns a distribution over plausible 3D
  bounding boxes. Subsequently, we render realistic objects and place them
  according to the locations sampled from the learned distribution. Our
  comprehensive evaluation on two standard datasets KITTI and NuScenes,
  demonstrates that MonoPlace3D significantly improves the accuracy of multiple
  existing monocular 3D detectors while being highly data efficient.
paper_authors._id: 67f8c967e096f2970b79fc45
paper_authors.name: Rishubh Parihar
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-11T08:37:25.657Z
paper_authors._id: 631af10b492c0c57a22ea3e2
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1662710000523-noauth.png
paper_authors.fullname: Rishubh Parihar
paper_authors.type: user
paper_authors.user: RishubhPar
paper_authors._id: 67f8c967e096f2970b79fc46
paper_authors.name: Srinjay Sarkar
paper_authors._id: 67f8c967e096f2970b79fc47
paper_authors.name: Sarthak Vora
paper_authors._id: 67f8c967e096f2970b79fc48
paper_authors.name: Jogendra Kundu
paper_authors._id: 67f8c967e096f2970b79fc49
paper_authors.name: R. Venkatesh Babu
paper_title: MonoPlace3D: Learning 3D-Aware Object Placement for 3D Monocular
    Detection
paper_summary: Current monocular 3D detectors are held back by the limited diversity and
  scale of real-world datasets. While data augmentation certainly helps, it's
  particularly difficult to generate realistic scene-aware augmented data for
  outdoor settings. Most current approaches to synthetic data generation focus on
  realistic object appearance through improved rendering techniques. However, we
  show that where and how objects are positioned is just as crucial for training
  effective 3D monocular detectors. The key obstacle lies in automatically
  determining realistic object placement parameters - including position,
  dimensions, and directional alignment when introducing synthetic objects into
  actual scenes. To address this, we introduce MonoPlace3D, a novel system that
  considers the 3D scene content to create realistic augmentations. Specifically,
  given a background scene, MonoPlace3D learns a distribution over plausible 3D
  bounding boxes. Subsequently, we render realistic objects and place them
  according to the locations sampled from the learned distribution. Our
  comprehensive evaluation on two standard datasets KITTI and NuScenes,
  demonstrates that MonoPlace3D significantly improves the accuracy of multiple
  existing monocular 3D detectors while being highly data efficient.
paper_ai_summary: MonoPlace3D enhances monocular 3D object detection by learning realistic object placements in scenes, improving detector accuracy with efficient data use.
paper_ai_keywords: monocular 3D detectors data augmentation synthetic data generation rendering techniques 3D bounding boxes