3D convolution is a fundamental operation in deep learning, particularly useful for processing volumetric data or sequences of 2D data like videos.
Essentially, 3D Convolution involves applying a 3D filter (also known as a kernel) across a 3D volume of data.
How 3D Convolution Works
According to the provided definition:
- A 3D filter slides over a 3D volume where the kernel size is less than the channel size.
- At each position as the filter slides, a specific calculation occurs: the value of each point within the filter is multiplied by the corresponding value in the underlying 3D volume.
- These multiplication results are then added together to produce a single output number for that specific position.
- This process is repeated as the filter moves across the entire 3D volume.
- Since the filter traverses through a 3D volume, the generated numbers also lie in a 3D space, forming the output volume.
Key Characteristics
- Input: Typically a 3D volume (e.g., height x width x depth/time x channels).
- Filter (Kernel): A small 3D cube with learnable weights (e.g., height x width x depth x channels).
- Operation: The 3D filter slides across all three spatial/temporal dimensions of the input volume.
- Calculation: Element-wise multiplication followed by summation within the filter's receptive field at each position.
- Output: A new 3D volume representing features extracted from the input.
- Kernel Size vs. Channel Size: As noted, in 3D convolution, the filter's spatial/temporal dimensions (kernel size) are typically smaller than the input's channel dimension.
Applications of 3D Convolution
3D convolution is particularly effective for tasks involving data with spatial and temporal or depth dependencies, such as:
- Video analysis (action recognition, video classification)
- Medical image analysis (MRI, CT scans)
- Volumetric data processing
This technique allows models to learn hierarchical representations that capture patterns not just within a single frame or slice, but also across sequences of frames or layers of a volume.
