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Monocular 3D Object Detection

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.

Papers

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Showing 1–10 of 187 papers

TitleStatusHype
SpikeSMOKE: Spiking Neural Networks for Monocular 3D Object Detection with Cross-Scale Gated Coding—0
MonoCoP: Chain-of-Prediction for Monocular 3D Object Detection—0
Weak Cube R-CNN: Weakly Supervised 3D Detection using only 2D Bounding Boxes—0
GATE3D: Generalized Attention-based Task-synergized Estimation in 3D*—0
MonoCT: Overcoming Monocular 3D Detection Domain Shift with Consistent Teacher Models—0
IROAM: Improving Roadside Monocular 3D Object Detection Learning from Autonomous Vehicle Data Domain—0
AuxDepthNet: Real-Time Monocular 3D Object Detection with Depth-Sensitive Features—0
Revisiting Monocular 3D Object Detection from Scene-Level Depth Retargeting to Instance-Level Spatial Refinement—0
V-MIND: Building Versatile Monocular Indoor 3D Detector with Diverse 2D Annotations—0
Open Vocabulary Monocular 3D Object DetectionCode2
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