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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 24, 2025

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

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NDDepth: Normal-Distance Assisted Monocular Depth Estimation and Completion.

Shuwei Shao, Zhongcai Pei, Weihai Chen

IEEE Transactions on Pattern Analysis and Machine Intelligence

|June 10, 2024

Summary

This summary is machine-generated.

This study introduces novel deep learning frameworks for monocular depth estimation and completion using physics-driven, piece-wise planar scene assumptions. The method estimates surface normals and distances, outperforming existing approaches on benchmark datasets.

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Area of Science:

Computer Vision
Deep Learning
3D Scene Understanding

Background:

Monocular depth estimation and completion are crucial computer vision tasks with broad applications.
Existing methods often directly estimate depth, which can be challenging with sparse or incomplete data.

Purpose of the Study:

To develop novel deep learning frameworks for monocular depth estimation and completion.
To leverage physics (geometry)-driven principles by assuming 3D scenes are composed of piece-wise planes.

Main Methods:

Proposed a framework that estimates surface normal and plane-to-origin distance maps as intermediate representations.
Developed a normal-distance head for pixel-level output and a plane-aware consistency constraint for regularization.
Integrated an additional depth head to enhance robustness and transformed intermediate outputs into depth maps.

Main Results:

The proposed method demonstrated superior performance compared to state-of-the-art competitors.
Experiments were conducted on widely recognized datasets: NYU-Depth-v2, KITTI, and SUN RGB-D.
The physics-driven approach proved effective for both depth estimation and completion tasks.

Conclusions:

The novel deep learning frameworks offer a robust and high-performing solution for monocular depth estimation and completion.
The assumption of piece-wise planar scenes and the use of intermediate surface normal and distance maps are key to the method's success.
This work advances the field of 3D scene understanding from monocular images.