3D Object Detection From Monocular Images
12 papers with code • 3 benchmarks • 3 datasets
This is the task of detecting 3D objects from monocular images (as opposed to LiDAR based counterparts). It is usually associated with autonomous driving based tasks.
( Image credit: Orthographic Feature Transform for Monocular 3D Object Detection )
Benchmarks
These leaderboards are used to track progress in 3D Object Detection From Monocular Images
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Waymo Open Dataset
KITTI-360
3D-POP
Most implemented papers
Deep Hough Voting for 3D Object Detection in Point Clouds
facebookresearch/votenet
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ICCV 2019
Current 3D object detection methods are heavily influenced by 2D detectors.
M3D-RPN: Monocular 3D Region Proposal Network for Object Detection
garrickbrazil/M3D-RPN
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ICCV 2019
Understanding the world in 3D is a critical component of urban autonomous driving.
DEVIANT: Depth EquiVarIAnt NeTwork for Monocular 3D Object Detection
As a result, DEVIANT is equivariant to the depth translations in the projective manifold whereas vanilla networks are not.
Orthographic Feature Transform for Monocular 3D Object Detection
tom-roddick/oft
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This allows us to reason holistically about the spatial configuration of the scene in a domain where scale is consistent and distances between objects are meaningful.
Delving into Localization Errors for Monocular 3D Object Detection
xinzhuma/monodle
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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
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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.
Geometry Uncertainty Projection Network for Monocular 3D Object Detection
In this paper, we propose a Geometry Uncertainty Projection Network (GUP Net) to tackle the error amplification problem at both inference and training stages.
ROCA: Robust CAD Model Retrieval and Alignment from a Single Image
We present ROCA, a novel end-to-end approach that retrieves and aligns 3D CAD models from a shape database to a single input image.
MonoDTR: Monocular 3D Object Detection with Depth-Aware Transformer
kuanchihhuang/monodtr
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CVPR 2022
Moreover, different from conventional pixel-wise positional encodings, we introduce a novel depth positional encoding (DPE) to inject depth positional hints into transformers.
MonoDETR: Depth-guided Transformer for Monocular 3D Object Detection
zrrskywalker/monodetr
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ICCV 2023
In this paper, we introduce the first DETR framework for Monocular DEtection with a depth-guided TRansformer, named MonoDETR.
