The field of 3D vision and scene reconstruction is rapidly advancing, with a focus on developing more efficient, accurate, and robust methods for reconstructing 3D scenes from 2D images and videos. Recent developments have centered around improving the accuracy and speed of 3D reconstruction algorithms, as well as enhancing their ability to handle complex and dynamic scenes. Notably, the use of multi-view consistency, test-time adaptation, and differentiable 3D transformations has shown promising results in improving the performance of 3D vision models. Furthermore, the development of more efficient and scalable algorithms, such as those using visual geometry grounded transformers, has enabled the reconstruction of large-scale scenes with high accuracy. Additionally, researchers have been exploring the application of 3D vision techniques to various domains, including robotics, autonomous driving, and endoscopic vision. Some noteworthy papers in this area include Muskie, which proposes a native multi-view vision backbone for 3D vision tasks, and MVS-TTA, which introduces a test-time adaptation framework for multi-view stereo methods. Overall, the field of 3D vision and scene reconstruction is rapidly evolving, with a focus on developing more efficient, accurate, and robust methods for reconstructing 3D scenes from 2D images and videos.