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Depth Perception and Spatial Vision

Depth Perception and Spatial Vision

Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.

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Related Experiment Video

Updated: Sep 21, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

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Published on: April 21, 2023

RT-ViT: Real-Time Monocular Depth Estimation Using Lightweight Vision Transformers.

Hatem Ibrahem¹, Ahmed Salem^1,2, Hyun-Soo Kang¹

¹Department of Information and Communication Engineering, School of Electrical and Computer Engineering, Chungbuk National University, Cheongju-si 28644, Korea.

Sensors (Basel, Switzerland)

|May 28, 2022

Summary

This summary is machine-generated.

This study introduces a real-time vision transformer (ViT) method for monocular depth estimation, achieving high accuracy and speed for indoor/outdoor scenes. The ViT-based encoder-decoder model enables fast 3D reconstruction.

Keywords:

convolutional neural networks monocular depth estimation real-time processing vision transformers

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Area of Science:

Computer Vision
Deep Learning
Artificial Intelligence

Background:

Vision Transformers (ViTs) offer global image understanding, outperforming traditional Convolutional Neural Networks (CNNs) in accuracy for various tasks.
However, the computational complexity of ViTs, particularly their fully connected layers, hinders real-time applications.
Monocular depth estimation from single RGB images is crucial for robotics and augmented reality.

Purpose of the Study:

To develop a real-time ViT-based monocular depth estimation method using an encoder-decoder architecture.
To optimize ViT models (ViT-b16, ViT-s16, ViT-t16) for speed while maintaining accuracy.
To evaluate the proposed method's performance against state-of-the-art approaches on benchmark datasets.

Main Methods:

An encoder-decoder architecture combining a Vision Transformer (ViT) encoder with a Convolutional Neural Network (CNN) decoder was proposed.
ViT models were progressively reduced in layers (12, 6, and 4) to achieve real-time processing speeds.
The models were trained end-to-end on NYU-depthV2 and CITYSCAPES datasets and evaluated for depth estimation accuracy and speed.

Main Results:

The ViT-based encoder-decoder architectures achieved state-of-the-art accuracy in monocular depth estimation.
The optimized ViT models (ViT-s16, ViT-t16) enabled real-time performance of approximately 20 frames per second.
Fast 3D reconstruction (approx. 17 fps) was demonstrated as a practical application of the estimated depth maps.

Conclusions:

The proposed real-time ViT-based method effectively addresses the speed limitations of ViTs for monocular depth estimation.
The encoder-decoder architecture leverages ViT's global context understanding and CNN's local feature extraction for superior performance.
This approach offers a viable solution for real-time computer vision applications requiring accurate depth perception and 3D reconstruction.