In recent years, visual relocalization has emerged as a pivotal task in the domains of 3D computer vision and learning based methodologies, witnessing substantial advances due to the evolution of learning based methodologies. This article reviews the current landscape and recent developments in visual relocalization research. It methodically discusses visual relocalization tasks, delineates fundamental solution methodologies, categorizes existing studies, and outlines research objectives within this field. By systematically organizing and elucidating the stateof-the-art of visual relocalization through the lens of learning based methodologies, this paper aims to provide a comprehensive analysis to aid researchers to swiftly grasp the essence of the research problem. It offers a lucid overview of the specific advances in various research directions, thereby facilitating effective applications and further investigation. Additionally, this article anticipates future research trajectories to address visual relocalization challenges.

In this paper, we present DEMC, a deep dual-encoder network to remove Monte Carlo noise efficiently while preserving details. Denoising Monte Carlo rendering is different from natural image denoising since inexpensive by-products (feature buffers) can be extracted in the rendering stage. Most of them are noise-free and can provide sufficient details for image reconstruction. However, these feature buffers also contain redundant information. Hence, the main challenge of this topic is how to extract useful information and reconstruct clean images. To address this problem, we propose a novel network structure, dual-encoder network with a feature fusion sub-network, to fuse feature buffers firstly, then encode the fused feature buffers and a noisy image simultaneously, and finally reconstruct a clean image by a decoder network. Compared with the state-of-the-art methods, our model is more robust on a wide range of scenes, and is able to generate satisfactory results in a significantly faster way.