Patch Sampling

Patch sampling is a technique used to efficiently process large datasets, particularly images and 3D point clouds, by selectively analyzing smaller, representative regions. Current research focuses on developing intelligent sampling strategies, often integrated with neural networks like Vision Transformers and neural radiance fields (NeRFs), to prioritize informative patches and reduce computational costs. This approach improves the efficiency and performance of various applications, including 3D reconstruction, reinforcement learning, image super-resolution, and quality assessment, by enabling faster training and inference while maintaining or even improving accuracy. The resulting advancements are significant for resource-constrained environments and large-scale data processing.