Advances in computational stereo

TLDR

Stereo vision reconstructs 3‑D scene structure from image pairs, a problem studied for decades that has evolved from basic correspondence and geometry to advanced methods enabling new, demanding applications. This review surveys recent computational stereo advances, concentrating on correspondence, occlusion handling, and real‑time implementation. The authors present comparative tables summarizing key ideas, distinctions among approaches, and suggest future analyses. Comparative analyses highlight strengths and gaps, and the authors propose directions for further investigation.

Abstract

Extraction of three-dimensional structure of a scene from stereo images is a problem that has been studied by the computer vision community for decades. Early work focused on the fundamentals of image correspondence and stereo geometry. Stereo research has matured significantly throughout the years and many advances in computational stereo continue to be made, allowing stereo to be applied to new and more demanding problems. We review recent advances in computational stereo, focusing primarily on three important topics: correspondence methods, methods for occlusion, and real-time implementations. Throughout, we present tables that summarize and draw distinctions among key ideas and approaches. Where available, we provide comparative analyses and we make suggestions for analyses yet to be done.