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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: Jun 23, 2025

Assessing Binocular Central Visual Field and Binocular Eye Movements in a Dichoptic Viewing Condition

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Published on: July 21, 2020

BinsFormer: Revisiting Adaptive Bins for Monocular Depth Estimation.

Zhenyu Li, Xuyang Wang, Xianming Liu

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

|June 24, 2024

Summary

BinsFormer improves monocular depth estimation by adaptively generating depth bins and enhancing interaction between predictions. This novel framework achieves state-of-the-art results on multiple benchmark datasets.

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Area of Science:

Computer Vision
Deep Learning

Background:

Monocular depth estimation (MDE) is crucial for understanding 3D scenes from 2D images.
Recent MDE methods leverage classification-regression approaches for improved performance.
These methods combine predicted probability distributions with discrete depth bins.

Purpose of the Study:

Introduce BinsFormer, a novel framework for classification-regression-based MDE.
Address key challenges: adaptive bin generation and probability-bin interaction.
Enhance spatial geometry understanding and estimation accuracy.

Main Methods:

Employ a Transformer decoder for direct set-to-set bin generation.
Integrate a multi-scale decoder for coarse-to-fine depth map estimation.
Utilize an auxiliary scene understanding query for improved accuracy.

Main Results:

BinsFormer demonstrates superior performance over state-of-the-art MDE methods.
Significant improvements observed on KITTI, NYU, and SUN RGB-D datasets.
The proposed methods effectively enhance adaptive bin generation and interaction.

Conclusions:

BinsFormer offers a powerful new approach to monocular depth estimation.
The Transformer-based bin generation and multi-scale decoding are effective.
Auxiliary scene understanding further boosts estimation accuracy.