The field of computer vision is witnessing significant advancements in dynamic scene reconstruction and understanding. Researchers are focusing on developing innovative methods to handle dynamic environments, which is a crucial aspect of various applications such as robotics, autonomous vehicles, and surgical video analysis. The current trend is towards designing approaches that can effectively capture temporal dynamics, handle object motions, and provide accurate reconstructions in the presence of moving objects. Notably, several papers have proposed novel techniques for 3D reconstruction, SLAM, and point cloud video recognition, which demonstrate superior performance in dynamic environments.

Some noteworthy papers in this regard include: Endo3R, which presents a unified 3D foundation model for online scale-consistent reconstruction from monocular surgical video. WildGS-SLAM, which introduces an uncertainty-aware geometric mapping approach for robust and efficient monocular RGB SLAM in dynamic environments. D^2USt3R, which proposes a method for enhancing 3D reconstruction with 4D pointmaps for dynamic scenes, demonstrating superior reconstruction performance across various datasets.