Monocular 3D Object Detection
74 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.
Libraries
Use these libraries to find Monocular 3D Object Detection models and implementationsLatest papers with no code
Decoupled Pseudo-labeling for Semi-Supervised Monocular 3D Object Detection
Additionally, we present a DepthGradient Projection (DGP) module to mitigate optimization conflicts caused by noisy depth supervision of pseudo-labels, effectively decoupling the depth gradient and removing conflicting gradients.
Multi-task Learning for Real-time Autonomous Driving Leveraging Task-adaptive Attention Generator
Real-time processing is crucial in autonomous driving systems due to the imperative of instantaneous decision-making and rapid response.
UniMODE: Unified Monocular 3D Object Detection
To address these challenges, we build a detector based on the bird's-eye-view (BEV) detection paradigm, where the explicit feature projection is beneficial to addressing the geometry learning ambiguity when employing multiple scenarios of data to train detectors.
You Only Look Bottom-Up for Monocular 3D Object Detection
Monocular 3D Object Detection is an essential task for autonomous driving.
Depth-discriminative Metric Learning for Monocular 3D Object Detection
Moreover, we introduce an auxiliary head for object-wise depth estimation, which enhances depth quality while maintaining the inference time.
Rotation Matters: Generalized Monocular 3D Object Detection for Various Camera Systems
In this paper, we conduct extensive experiments to analyze the factors that cause performance degradation.
Every Dataset Counts: Scaling up Monocular 3D Object Detection with Joint Datasets Training
Monocular 3D object detection plays a crucial role in autonomous driving.
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.