Monocular 3D Object Detection
77 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
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
Libraries
Use these libraries to find Monocular 3D Object Detection models and implementations
KITTI
SUN RGB-D
Virtual KITTI
OPV2V
Virtual KITTI 2
Most implemented papers
MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships
Nicholasli1995/EgoNet
•
•
CVPR 2020
Monocular 3D object detection is an essential component in autonomous driving while challenging to solve, especially for those occluded samples which are only partially visible.
Monocular 3D Object Detection with Sequential Feature Association and Depth Hint Augmentation
gtzly/FADNet
•
•
In this work, a unified network named as FADNet is presented to address the task of monocular 3D object detection.
Objectron: A Large Scale Dataset of Object-Centric Videos in the Wild with Pose Annotations
google-research-datasets/Objectron
•
•
CVPR 2021
3D object detection has recently become popular due to many applications in robotics, augmented reality, autonomy, and image retrieval.
Ground-aware Monocular 3D Object Detection for Autonomous Driving
Owen-Liuyuxuan/visualDet3D
•
•
We further verify the power of the proposed module with a neural network designed for monocular depth prediction.
Holistic 3D Scene Understanding from a Single Image with Implicit Representation
chengzhag/Implicit3DUnderstanding
•
•
CVPR 2021
We not only propose an image-based local structured implicit network to improve the object shape estimation, but also refine the 3D object pose and scene layout via a novel implicit scene graph neural network that exploits the implicit local object features.
MonoRUn: Monocular 3D Object Detection by Reconstruction and Uncertainty Propagation
tjiiv-cprg/MonoRUn
•
•
CVPR 2021
To regress the pixel-related 3D object coordinates, we employ a regional reconstruction network with uncertainty awareness.
M3DSSD: Monocular 3D Single Stage Object Detector
mumianyuxin/M3DSSD
•
•
CVPR 2021
In the first step, the shape alignment is performed to enable the receptive field of the feature map to focus on the pre-defined anchors with high confidence scores.
Depth-conditioned Dynamic Message Propagation for Monocular 3D Object Detection
The objective of this paper is to learn context- and depth-aware feature representation to solve the problem of monocular 3D object detection.
Delving into Localization Errors for Monocular 3D Object Detection
xinzhuma/monodle
•
•
CVPR 2021
Estimating 3D bounding boxes from monocular images is an essential component in autonomous driving, while accurate 3D object detection from this kind of data is very challenging.
GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Monocular 3D Object Detection
abhi1kumar/groomed_nms
•
•
CVPR 2021
In this paper, we present and integrate GrooMeD-NMS -- a novel Grouped Mathematically Differentiable NMS for monocular 3D object detection, such that the network is trained end-to-end with a loss on the boxes after NMS.
