Monocular 3D Detection with Geometric Constraints Embedding and Semi-supervised Training

Understand

In this work, we propose a novel single-shot and keypoints-based framework for monocular 3D objects detection using only RGB images, called KM3D-Net.

• We design a fully convolutional model to predict object keypoints, dimension, and orientation, and then combine these estimations with perspective geometry constraints to compute position attribute.
• Further, we reformulate the geometric constraints as a differentiable version and embed it into the network to reduce running time while maintaining the consistency of model outputs in an end-to-end fashion.
• Benefiting from this simple structure, we then propose an effective semi-supervised training strategy for the setting where labeled training data is scarce.