What is it about?

It is an efficient monocular 3D object detection method that combines Mask-Revised Network and Quality Perception Loss. This method can adaptively encode the image into a mask to reduce the background noise and realize quality perception of the prediction results.

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Why is it important?

Due to the lack of depth information and the inaccuracy of depth estimation, the accuracy of monocular 3D object detection is limited. Therefore, the combination of 2D object detection technology and quality perception constraints achieves a high-precision 3D bounding box for predicting object.


The proposed method combines the attention mechanism to realize the fusion of image features and depth features, which improves the detection accuracy of 3D object.

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Read the Original

This page is a summary of: Monocular 3D object detection via Mask‐Revised Network and quality perception loss, IET Computer Vision, November 2022, the Institution of Engineering and Technology (the IET),
DOI: 10.1049/cvi2.12157.
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