The field of scene understanding and localization is rapidly advancing, with a focus on improving the accuracy and robustness of scene coordinate regression models. Recent developments have seen the introduction of novel methods for infusing high-level reconstruction priors into these models, allowing for more coherent scene point clouds and improved camera pose estimation. Additionally, there has been a push towards improving the generalization capabilities of these models, enabling them to perform well even in the presence of significant changes in imaging conditions. Another area of research has been the development of more efficient and scalable methods for large-scale scene localization and rendering, with a focus on reducing computational costs while maintaining high precision. Noteworthy papers include:

• Into the Unknown, which presents a sampling-based pipeline leveraging generative models to produce probabilistic priors for planning in configuration spaces.
• ACE-G, which proposes a pre-training method to improve the generalization of scene coordinate regression models.
• MACE, which introduces a mixture-of-experts approach for accelerated coordinate encoding in large-scale scenes.