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Related Experiment Video

Updated: May 8, 2026

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

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

Published on: December 3, 2013

Depth spreading through empty space induced by sparse disparity cues.

Xintong Li¹, Abigail E Huang, Eric L Altschuler

¹Rutgers New Jersey Medical School, Newark, NJ, USA. xl253@njms.rutgers.edu

Journal of Vision

|August 16, 2013

Summary

This summary is machine-generated.

The human visual system interpolates 3D shapes using depth cues. Our study shows this depth spreading relies on high-level object recognition, not just local edge information.

Keywords:

3D surface and shape perception Bayesian prior depth perception disparity filling-in propagation

More Related Videos

Digital Inline Holographic Microscopy (DIHM) of Weakly-scattering Subjects

Digital Inline Holographic Microscopy (DIHM) of Weakly-scattering Subjects

Published on: February 8, 2014

Related Experiment Videos

Last Updated: May 8, 2026

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

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

Published on: December 3, 2013

Digital Inline Holographic Microscopy (DIHM) of Weakly-scattering Subjects

Digital Inline Holographic Microscopy (DIHM) of Weakly-scattering Subjects

Published on: February 8, 2014

Area of Science:

Visual perception
Computational neuroscience
3D spatial understanding

Background:

Visual processing aims to reconstruct 3D object layouts from incomplete depth cues.
Binocular disparity is a key depth cue, but it's often sparse and limited to edges.

Purpose of the Study:

Investigate how the visual system interpolates depth across empty space.
Test hypotheses of depth spreading from disparity edges.

Main Methods:

Studied depth spreading using an eight-spoke configuration, similar to neon color spreading.
Proposed and tested four hypotheses: local, linear, and figural depth propagation.

Main Results:

Perceived depth and contour position data rejected low-level and mid-level hypotheses.
Results strongly supported a high-level figural depth propagation model.

Conclusions:

Depth spreading is a quantifiable perceptual process.
It propagates depth contours anisotropically, guided by high-level figural properties of 3D objects.