Voxel Selection

Voxel selection focuses on strategically choosing subsets of 3D data points (voxels) for improved efficiency and accuracy in various applications, such as medical image segmentation, scene completion, and 3D object detection. Current research emphasizes methods that account for voxel "hardness" – the difficulty of classifying or segmenting individual voxels – often employing techniques like active learning, self-distillation, and loss functions that weight voxels based on their uncertainty or importance. These advancements lead to more efficient training and improved performance in tasks involving large, complex 3D datasets, impacting fields ranging from autonomous driving to medical diagnostics.