Conv3D Output Size Calculator (Video & Medical)
Output D×H×W of 3-D convolutions for video clips and CT/MRI volumes — with voxel/frame budgeting.
Defaults are a C3D/R3D video block: 16-frame 112×112 clips with 3×3×3 kernels. Medical volumes (e.g. 64×512×512 CT) use the same arithmetic with anisotropic kernels because slice spacing ≠ pixel spacing.
Formula
About Conv3D Output Size Calculator (Video & Medical)
Add a third axis and convolution arithmetic stays the same — but the budgets explode, which is why every video and medical-imaging architect lives in this calculator's regime. Each axis (time or slice depth, height, width) applies the floor-division formula independently, and the voxel count output shows why: a modest 16×112×112 clip already produces 200K voxels per channel per layer. The defaults match C3D-style video blocks; radiology volumes use the identical math with anisotropic kernels because CT slice spacing rarely equals in-plane pixel spacing.
How to use Conv3D Output Size Calculator (Video & Medical)
- 1Enter your values into Conv3D Output Size Calculator (Video & Medical) — sensible, domain-typical defaults are pre-filled so you see a real result immediately.
- 2The result recomputes live using the formula shown on the page; there is no button to press.
- 3Adjust any input to compare scenarios, then read the worked example to see the substituted numbers.
Why use Conv3D Output Size Calculator (Video & Medical)?
- ✓Computes Conv3D Output Size instantly in your browser — no sign-up, no upload, no server round-trip.
- ✓100% free and unlimited, with the exact formula shown: each axis independently: out = ⌊(in + 2p − k) / s⌋ + 1 — depth (time/slices) and space may use different k, s.
- ✓Runs entirely client-side, so every value you enter stays private on your device.
- ✓Live recompute as you type, with a worked example and authoritative references for trust.
Frequently asked questions
Why do video models use different temporal and spatial strides?+
Information density differs: adjacent frames are nearly identical while spatial detail matters, so architectures like R(2+1)D and SlowFast downsample space aggressively but keep temporal resolution longer (or split into slow/fast pathways). Separate kt/st fields here model exactly that.
What is a (2+1)D convolution?+
A factorization: a 1×k×k spatial conv followed by a kt×1×1 temporal conv, replacing the full 3-D kernel. It cuts parameters and adds a nonlinearity between space and time, outperforming plain 3-D convs at equal budget in the R(2+1)D paper.
How should kernels handle anisotropic medical volumes?+
Match the physical spacing: with 5 mm slices and 1 mm pixels, a 3×3×3 kernel spans 15×3×3 mm — badly lopsided. Common fixes are 1×3×3 kernels in early layers or resampling the volume to isotropic spacing before the network.
Why does 3-D conv memory explode so fast?+
Activations scale with D×H×W per channel: doubling each axis is 8× memory, and backprop stores activations per layer. This is why 3-D U-Nets train on patches (e.g. 96³ crops) rather than whole CT volumes, stitching predictions at inference.
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