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
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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 with no code
MonoGAE: Roadside Monocular 3D Object Detection with Ground-Aware Embeddings
We discover that most existing monocular 3D object detectors rely on the ego-vehicle prior assumption that the optical axis of the camera is parallel to the ground.
Polygon Intersection-over-Union Loss for Viewpoint-Agnostic Monocular 3D Vehicle Detection
Monocular 3D object detection is a challenging task because depth information is difficult to obtain from 2D images.
S$^3$-MonoDETR: Supervised Shape&Scale-perceptive Deformable Transformer for Monocular 3D Object Detection
These methods typically use visual and depth representations to generate query points on objects, whose quality plays a decisive role in the detection accuracy.
MonoNext: A 3D Monocular Object Detection with ConvNext
Autonomous driving perception tasks rely heavily on cameras as the primary sensor for Object Detection, Semantic Segmentation, Instance Segmentation, and Object Tracking.
Monocular 3D Object Detection with LiDAR Guided Semi Supervised Active Learning
We propose a novel semi-supervised active learning (SSAL) framework for monocular 3D object detection with LiDAR guidance (MonoLiG), which leverages all modalities of collected data during model development.
Understanding Depth Map Progressively: Adaptive Distance Interval Separation for Monocular 3d Object Detection
In this paper, we propose a framework named the Adaptive Distance Interval Separation Network (ADISN) that adopts a novel perspective on understanding depth maps, as a form that lies between LiDAR and images.
Weakly Supervised 3D Object Detection with Multi-Stage Generalization
We devise the DoubleClustering algorithm to obtain object clusters from reconstructed scene-level points, and further enhance the model's detection capabilities by developing three stages of generalization: progressing from complete to partial, static to dynamic, and close to distant.
Monocular 2D Camera-based Proximity Monitoring for Human-Machine Collision Warning on Construction Sites
This study preliminarily reveals the potential and feasibility of proximity monitoring using only a 2D camera, providing a new promising and economical way for early warning of human-machine collisions.
MonoTDP: Twin Depth Perception for Monocular 3D Object Detection in Adverse Scenes
3D object detection plays a crucial role in numerous intelligent vision systems.
Monocular 3D Object Detection with Bounding Box Denoising in 3D by Perceiver
Motivated by a new and strong observation that this challenge can be remedied by a 3D-space local-grid search scheme in an ideal case, we propose a stage-wise approach, which combines the information flow from 2D-to-3D (3D bounding box proposal generation with a single 2D image) and 3D-to-2D (proposal verification by denoising with 3D-to-2D contexts) in a top-down manner.
