mickeyvanolst/depthpro-coreml

Depth Pro — Core ML (metric depth, 1024, 6-bit palettized)

A Core ML conversion of Apple's Depth Pro — metric monocular depth: absolute metres from a single image, with no camera intrinsics, plus an estimated focal length.

Converted for AML (TD Apple ML), where it runs in the CoreML TOP inside TouchDesigner, but there is nothing TouchDesigner-specific about the package.

What it gives you

output	shape	meaning
depth_m	[1, 1, 1024, 1024]	depth in metres
focallength_px	[1]	estimated focal length in pixels

Input: image, 1536×1536 RGB (the network's own size — the depth map is resized to 1024 inside the graph, while the values are still float).

How it was converted

1. depth_pro.pt from Apple's CDN (the repo's own get_pretrained_models.sh).
2. Traced at 1536×1536 and converted with coremltools 9 (fp16 compute). deg2rad has no Core ML conversion and was replaced with a multiply.
3. The metric arithmetic from DepthPro.infer is baked into the graph, so the model returns metres rather than canonical inverse depth: f_px = 0.5·W / tan(0.5·fov), depth = 1 / (canonical · W / f_px).
4. The depth map is resized to 1024² in-graph.
5. Weights palettized to 6 bits (kmeans, per-tensor).

Accuracy and cost

Measured against the PyTorch reference on the same photograph, Apple silicon (fanless M-series):

	size	per frame	vs PyTorch
fp16	1.8 GB	4.8 s	median 0.15 %
6-bit (this package)	682 MB	3.6 s	median 1.17 %, p95 4.5 %

First load compiles for the Neural Engine and takes a couple of minutes; afterwards it is cached by the OS.

Licence

Apple's licence for Depth Pro, redistributed verbatim as LICENSE. The weights come from the repository's own download script; this package is a format conversion of them.

Citation

@inproceedings{Bochkovskii2025:depthpro,
  title     = {Depth Pro: Sharp Monocular Metric Depth in Less Than a Second},
  author    = {Aleksei Bochkovskii and Ama\"{e}l Delaunoy and Hugo Germain and
               Marcel Santos and Yichao Zhou and Stephan R. Richter and
               Vladlen Koltun},
  booktitle = {International Conference on Learning Representations},
  year      = {2025},
}