Monocular 3D Object Detection
76 papers with code • 15 benchmarks • 5 datasets
Monocular 3D Object Detection is the task to draw 3D bounding box around objects in a single 2D RGB image. It is localization task but without any extra information like depth or other sensors or multiple-images.
Benchmarks
These leaderboards are used to track progress in Monocular 3D Object Detection
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Libraries
Use these libraries to find Monocular 3D Object Detection models and implementations
KITTI
SUN RGB-D
Virtual KITTI
OPV2V
Virtual KITTI 2
Latest papers
MonoCD: Monocular 3D Object Detection with Complementary Depths
Monocular 3D object detection has attracted widespread attention due to its potential to accurately obtain object 3D localization from a single image at a low cost.
VSRD: Instance-Aware Volumetric Silhouette Rendering for Weakly Supervised 3D Object Detection
skmhrk1209/VSRD
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In the auto-labeling stage, we represent the surface of each instance as a signed distance field (SDF) and render its silhouette as an instance mask through our proposed instance-aware volumetric silhouette rendering.
MonoLSS: Learnable Sample Selection For Monocular 3D Detection
To select samples adaptively, we propose a Learnable Sample Selection (LSS) module, which is based on Gumbel-Softmax and a relative-distance sample divider.
3D Copy-Paste: Physically Plausible Object Insertion for Monocular 3D Detection
A major challenge in monocular 3D object detection is the limited diversity and quantity of objects in real datasets.
ODM3D: Alleviating Foreground Sparsity for Semi-Supervised Monocular 3D Object Detection
arcaninez/odm3d
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Monocular 3D object detection (M3OD) is a significant yet inherently challenging task in autonomous driving due to absence of explicit depth cues in a single RGB image.
GUPNet++: Geometry Uncertainty Propagation Network for Monocular 3D Object Detection
supermhp/gupnet
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It models the uncertainty propagation relationship of the geometry projection during training, improving the stability and efficiency of the end-to-end model learning.
MonoSKD: General Distillation Framework for Monocular 3D Object Detection via Spearman Correlation Coefficient
Monocular 3D object detection is an inherently ill-posed problem, as it is challenging to predict accurate 3D localization from a single image.
CoBEV: Elevating Roadside 3D Object Detection with Depth and Height Complementarity
MasterHow/CoBEV
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Roadside camera-driven 3D object detection is a crucial task in intelligent transportation systems, which extends the perception range beyond the limitations of vision-centric vehicles and enhances road safety.
MonoUNI: A Unified Vehicle and Infrastructure-side Monocular 3D Object Detection Network with Sufficient Depth Clues
Traffic-X/MonoUNI
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Neural Information Processing Systems 2023
Monocular 3D detection of vehicle and infrastructure sides are two important topics in autonomous driving.
Perspective-aware Convolution for Monocular 3D Object Detection
KenYu910645/perspective-aware-convolution
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Monocular 3D object detection is a crucial and challenging task for autonomous driving vehicle, while it uses only a single camera image to infer 3D objects in the scene.
