MonoPlace3D: Learning 3D-Aware Object Placement for 3D Monocular
  Detection
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.
67f8c967e096f2970b79fc45
Rishubh Parihar
admin_assigned
2025-04-11T08:37:25.657Z
631af10b492c0c57a22ea3e2
https://cdn-avatars.huggingface.co/v1/production/uploads/1662710000523-noauth.png
Rishubh Parihar
user
RishubhPar
67f8c967e096f2970b79fc46
Srinjay Sarkar
67f8c967e096f2970b79fc47
Sarthak Vora
67f8c967e096f2970b79fc48
Jogendra Kundu
67f8c967e096f2970b79fc49
R. Venkatesh Babu
MonoPlace3D: Learning 3D-Aware Object Placement for 3D Monocular
  Detection
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.
MonoPlace3D enhances monocular 3D object detection by learning realistic object placements in scenes, improving detector accuracy with efficient data use.
monocular 3D detectors data augmentation synthetic data generation rendering techniques 3D bounding boxes