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Related Experiment Video

Updated: Jul 30, 2025

Measuring Sensitivity to Viewpoint Change with and without Stereoscopic Cues

Measuring Sensitivity to Viewpoint Change with and without Stereoscopic Cues

Published on: December 4, 2013

Stereoscopic Vision Recalling Memory for Monocular 3D Object Detection.

Jung Uk Kim, Hyung-Il Kim, Yong Man Ro

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|May 12, 2023

Summary

This summary is machine-generated.

This study introduces a novel monocular 3D object detection method that recalls stereoscopic vision using Monocular-to-Stereoscopic (M2S) memory. This approach effectively reconstructs 3D information from single images, mimicking human visual recall.

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Last Updated: Jul 30, 2025

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Area of Science:

Computer Vision
Machine Learning

Background:

Monocular 3D object detection is crucial for applications like autonomous driving.
Single images lack sufficient depth and stereoscopic cues for accurate 3D inference.

Purpose of the Study:

To develop a monocular 3D object detector capable of recalling stereoscopic visual information from a single image.
To enhance 3D perception by inferring depth and right-view appearance from a left-view image.

Main Methods:

A location embedding module processes objects based on their position.
Monocular-to-Stereoscopic (M2S) memory recalls right-view appearance and depth.
Stereoscopic vision memorizing loss and binocular vision association loss train the M2S memory.

Main Results:

The proposed M2S memory effectively exploits recalled stereoscopic information during inference.
Experiments on KITTI and Waymo datasets validate the method's effectiveness.
The detector achieves improved 3D object detection performance using monocular input.

Conclusions:

The developed monocular 3D object detector successfully recalls and utilizes stereoscopic information.
This method offers a significant advancement in monocular 3D perception, inspired by human visual capabilities.