Rendu bas\'e image avec contraintes sur les gradients

TL;DR

This paper introduces a variational method that imposes gradient constraints to improve multi-view image rendering, effectively reducing artifacts caused by scene discontinuities and outperforming existing techniques.

Contribution

It proposes a novel gradient-based regularization framework for multi-view rendering that enhances image quality and artifact removal compared to prior methods.

Findings

Numerically outperforms state-of-the-art methods

Eliminates artifacts from visibility discontinuities

Works on structured and unstructured datasets

Abstract

Multi-view image-based rendering consists in generating a novel view of a scene from a set of source views. In general, this works by first doing a coarse 3D reconstruction of the scene, and then using this reconstruction to establish correspondences between source and target views, followed by blending the warped views to get the final image. Unfortunately, discontinuities in the blending weights, due to scene geometry or camera placement, result in artifacts in the target view. In this paper, we show how to avoid these artifacts by imposing additional constraints on the image gradients of the novel view. We propose a variational framework in which an energy functional is derived and optimized by iteratively solving a linear system. We demonstrate this method on several structured and unstructured multi-view datasets, and show that it numerically outperforms state-of-the-art methods, and eliminates artifacts that result from visibility discontinuities