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 with no code
VFMM3D: Releasing the Potential of Image by Vision Foundation Model for Monocular 3D Object Detection
Therefore, an effective solution involves transforming monocular images into LiDAR-like representations and employing a LiDAR-based 3D object detector to predict the 3D coordinates of objects.
MOSE: Boosting Vision-based Roadside 3D Object Detection with Scene Cues
3D object detection based on roadside cameras is an additional way for autonomous driving to alleviate the challenges of occlusion and short perception range from vehicle cameras.
MonoTAKD: Teaching Assistant Knowledge Distillation for Monocular 3D Object Detection
Subsequently, we introduce the cross-modal residual distillation to transfer the 3D spatial cues.
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.
