Retrieval Based Reconstruction

Retrieval-based reconstruction leverages the power of retrieved information to improve the accuracy and efficiency of reconstructing various data types, from 3D objects to time series. Current research focuses on integrating retrieval methods with deep learning models, such as transformers and contrastive learning frameworks, often employing techniques like Monte Carlo Tree Search for optimization and combining global and local feature descriptors for robustness. This approach shows promise in enhancing 3D scene understanding, improving the reconstruction of challenging scenarios (e.g., hand-held objects), and advancing self-supervised learning by providing a novel way to define positive data pairs for contrastive learning.