Computer Vision For Detecting 3D Objects | 3D Object Detection

This post is about using computer vision to detect 3D objects, more specifically about 3D virtual object detection.

Introduction

Computer vision algorithms for detecting 3D objects typically involve multiple steps. Here’s a high-level overview of how computer vision works to detect 3D objects:

Image acquisition: The process begins with acquiring one or more images or frames from a camera or sensor. These images can be 2D representations of the 3D scene.

Preprocessing: The acquired images may undergo preprocessing steps to enhance their quality or remove noise. Common preprocessing techniques include resizing, filtering, and adjusting contrast or brightness.

Feature extraction: In this step, relevant features are extracted from the preprocessed images. Features could include edges, corners, texture patterns, or other visual descriptors. Feature extraction methods can vary based on the specific object detection algorithm being used.

Feature matching: Once features are extracted, the algorithm compares them with predefined templates or descriptors representing the 3D objects of interest. This matching process helps identify potential object instances in the scene.