The field of 3D computer vision is rapidly advancing, with a focus on improving the accuracy and efficiency of 3D reconstruction and symmetry detection. Recent research has explored the use of visual features, prior knowledge, and multi-view information to enhance the performance of 3D reconstruction models. Additionally, innovative approaches such as zero-shot learning and transformer-based models have been proposed to tackle challenging tasks like 3D plane reconstruction and object completion. Notably, the development of rig-aware models and line-based 3D representations has shown promise in improving the robustness and accuracy of 3D reconstruction. Overall, the field is moving towards more efficient, accurate, and generalizable 3D reconstruction and symmetry detection methods. Noteworthy papers include: Rig3R, which introduces a rig-aware conditioning approach for learned 3D reconstruction, achieving state-of-the-art performance in 3D reconstruction, camera pose estimation, and rig discovery. Towards In-the-wild 3D Plane Reconstruction from a Single Image, which proposes a novel framework for zero-shot 3D plane detection and reconstruction from a single image, demonstrating significant improvements in reconstruction accuracy and generalizability.