# Visual Mesh: Real-time Object Detection Using Constant Sample Density

Source: [Visual Mesh: Real-time Object Detection Using Constant Sample Density](<https://www.sota2.com/research/paper/68fa649a1e209131cea056e8>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-27T05:54:50.233Z

Authors: Trent Houliston, Stephan K. Chalup

Published: 2018-07-23T02:21:31.000Z

## Abstract

This paper proposes an enhancement of convolutional neural networks for object detection in resource-constrained robotics through a geometric input transformation called Visual Mesh. It uses object geometry to create a graph in vision space, reducing computational complexity by normalizing the pixel and feature density of objects. The experiments compare the Visual Mesh with several other fast convolutional neural networks. The results demonstrate execution times sixteen times quicker than the fastest competitor tested, while achieving outstanding accuracy.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1807.08405>)

[PDF](<https://arxiv.org/pdf/1807.08405.pdf>)