Depth Perception Optimization Method Based on Scale Variation
LIU Xizuo
WANG Zhenjiang
WANG Hao
Abstract：Compared to the real world,users observing virtual environments may experience inconsistent depth perception,which hampers the application of virtual simulation systems in training scenarios.To mitigate or eliminate these discrepancies,this paper proposes a depth perception optimization method based on scale variation.By establishing a depth perception mapping model between real and virtual observation spaces through scale variations induced by field-of-view(FOV)scaling in virtual scenes,the method maps corresponding depth perception relationships to the scaling adjustments of large virtual objects.This enhances depth perception effects for trainees.Additionally,to address the limited visible range of small objects in virtual scenes due to restricted display resolution,their physical dimensions are appropriately enlarged.This expands their visible range without significantly im-pacting depth estimation,thereby improving visibility distances consistent with real-world scenes.Tests conducted on a marine sim-ulator demonstrate that the proposed method effectively enhances users'depth perception experience.
Keywords：virtual environmentobservation spacedepth perceptionsimulation trainingscale mapping model
Publication Date：2025-11-20
Online Publishing Date：2026-01-28(First online date of this platform, not the publication date of the document)
Pages：6( 133-138 )