Depth and Dimension Estimation Using Computer Vision

Abstract

The growing importance of depth and dimension estimation in robotics, autonomous vehicles, and construction industries highlights the need for accurate environmental perception. Monocular depth-based cameras face challenges such as scale ambiguity and data scarcity, making precise distance measurements difficult. Stereo-vision setups provide improved scene information, thereby reducing the error rate. Deriving depth from stereo images using a Pseudo-LIDAR based approach enhances depth data. Pixel-to-length conversion from this depth information enables precise dimension estimation of objects. Developing a computer vision algorithm that calculates pixel-to-length ratios at various distances and adjusting for calibration discrepancies will streamline decision-making in autonomous systems, improving efficiency across industrial operations.