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Related Concept Videos

Depth Perception and Spatial Vision01:15

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: May 14, 2026

A Gaze-Contingent Display Framework for Perceptual Learning Research with Simulated Central Vision Loss
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Published on: April 11, 2025

Visual Place Recognition Based on an Adaptive D-Value Optimization Strategy.

Yu-Hong Jian1,2, Jin-Shyan Lee1

  • 1Department of Electrical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan.

Sensors (Basel, Switzerland)
|May 13, 2026
PubMed
Summary
This summary is machine-generated.

A fixed focal distance in visual place recognition (VPR) limits performance. This study introduces a depth-aware adaptive strategy, improving VPR reliability by adjusting focal distances based on scene geometry for better environmental perception.

Keywords:
EigenPlacesadaptive focal distancedepth estimationquantile mappingtraining label generationvisual place recognition

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Area of Science:

  • Computer Vision
  • Robotics
  • Machine Learning

Background:

  • Visual Place Recognition (VPR) methods like EigenPlaces use Singular Value Decomposition (SVD) based focal points for training.
  • A fixed focal distance (D) in VPR training is suboptimal due to varying urban scene geometries.
  • The optimal focal distance (D) is dataset-dependent, impacting VPR performance.

Purpose of the Study:

  • To analyze the sensitivity of the fixed focal distance (D) in EigenPlaces across diverse datasets.
  • To develop an adaptive focal distance strategy that improves VPR performance in varied environments.
  • To enhance the environmental perception reliability of intelligent sensing platforms.

Main Methods:

  • Systematic analysis of focal distance (D) sensitivity across multiple benchmark datasets.
  • Proposal of a depth-aware adaptive D strategy using monocular depth estimation for per-cell focal distances.
  • Integration of quantile mapping to ensure variance in assigned D values and principled connection between visual data and geometric supervision.

Main Results:

  • The optimal focal distance (D) is confirmed to be highly dataset-dependent, with performance variations up to 4.4%.
  • The proposed depth-aware adaptive D strategy achieved the best same-distribution performance on Pitts30k, AmsterTime, and SF-XL benchmarks.
  • Ablation studies provided practical guidance for adaptive focal distance selection in VPR training.

Conclusions:

  • Adaptive focal distance selection is crucial for robust VPR performance across different urban environments.
  • Depth-aware strategies offer a principled way to connect visual data with geometric training supervision for VPR.
  • The developed method enhances the reliability and adaptability of VPR systems for intelligent platforms.