filtervolume

In computing and data processing, filtervolume refers to the three-dimensional support of a spatial filter applied to volumetric data; the set of input samples that influence a single output voxel after convolution with a three-dimensional kernel.

A filtervolume is defined by the kernel size and shape, often a cube or sphere, with a radius or half-width expressed in voxels or physical units. Filtering proceeds by sliding the kernel through the volume and computing a weighted sum, producing an output voxel whose value reflects contributions from neighboring input voxels within the filtervolume.

Common kernels include Gaussian, box, and edge-detection kernels. In performance-oriented pipelines, separable kernels allow the 3D filter to be broken into three 1D passes, reducing compute and memory traffic.

Filter volumes are central to medical imaging (denoising CT and MRI data), scientific visualization, and computer graphics. They are also integral to three-dimensional convolutional neural networks, where the learned filter volumes detect features in volumetric data.

Implementation considerations include boundary handling, interpolation, and padding, as well as memory usage and parallelization on GPUs. The filtervolume size determines the receptive field for the output voxel and influences smoothing strength and feature preservation.

Related concepts include convolution, 3D kernels, and volume data, with common practical variants such as Gaussian blur, box filtering, and edge-detection filters used across applications.