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NOTICE

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+Depth Anything 3 Small Multi-View Core ML
+Copyright 2026 Shuichi Tsutsumi
+
+This repository contains Core ML conversions of the Depth Anything 3 Small model weights.
+
+Original work:
+  Depth Anything 3 Small (depth-anything/DA3-SMALL on Hugging Face,
+  revision e08cab65ca0ec38e7826075418411ab90cab4da3), licensed under the Apache License, Version 2.0.
+  The original repository has no NOTICE file.
+
+Changes made in this conversion:
+  - Converted from PyTorch to Core ML packages (.mlpackage) with coremltools.
+  - Multi-view input with camera poses: the models take 2 or 3 images together with each image's
+    extrinsics and intrinsics (for example from ARKit), instead of a single image.
+  - Fixed input sizes of 504 x 504 or 392 x 392 pixels.
+  - In the "sliced" variants, the global attention across views is computed in slices of the query
+    (16 slices, or 8 in "sliced8") to keep the intermediate matrices small.
+  - In the "headcombo" variants, the depth head was rewritten (transposed-convolution upsampling, and the
+    upsampling moved later in the head). Depth values differ slightly from the original at object edges.
+
+The full text of the Apache License, Version 2.0 is in the LICENSE file.

README.md

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+---
+license: apache-2.0
+base_model: depth-anything/DA3-SMALL
+library_name: coreml
+pipeline_tag: depth-estimation
+tags:
+  - coreml
+  - core-ml
+  - depth-estimation
+  - depth-anything-3
+  - DA3
+  - multi-view
+  - multi-view-geometry
+  - pose-conditioned
+  - arkit
+  - ios
+  - iphone
+  - apple-neural-engine
+  - on-device
+---
+
+# Depth Anything 3 (DA3) Small — Multi-View, Pose-Conditioned Core ML for iPhone
+
+Core ML packages of [Depth Anything 3 Small](https://huggingface.co/depth-anything/DA3-SMALL) (`DA3-SMALL`,
+revision `e08cab65ca0ec38e7826075418411ab90cab4da3`) that run the **multi-view, pose-conditioned** path of the
+network on iPhone. Each model takes 2 or 3 images together with their camera poses (extrinsics and intrinsics, for
+example from ARKit) and returns, for every image, a depth map, a confidence map and the camera pose the network
+estimates. The variants are rewritten to run faster and use less memory on the iPhone Neural Engine than a plain
+conversion.
+
+Existing Core ML conversions of DA3 Small take a single image. These packages keep the camera encoder and decoder,
+so depth from several views is consistent across the views and can be brought to metres with the input poses.
+
+## Variants
+
+| Package | Views | Input | What is changed | Minimum iOS | Size |
+|---|---|---|---|---|---|
+| `models/mv3pose392_sliced8_headcombo.mlpackage` (recommended) | 3 | 392 × 392 | 392 input, global attention in 8 slices, depth head rewritten | 18.0 | 72.0 MB |
+| `models/mv2pose392_sliced_headcombo.mlpackage` | 2 | 392 × 392 | 392 input, global attention in 16 slices, depth head rewritten | 18.0 | 71.8 MB |
+| `models/mv3pose_sliced.mlpackage` | 3 | 504 × 504 | global attention in 16 slices | 18.0 | 86.8 MB |
+| `models/mv3pose.mlpackage` | 3 | 504 × 504 | none (plain conversion of the pose-conditioned path) | 17.0 | 86.7 MB |
+
+All packages are FP16.
+
+- **Sliced global attention** (`sliced`, `sliced8`): the attention across all views is computed in slices of the
+  query, so the intermediate attention matrices stay small. It only changes the order of the operations: the
+  weights are identical (`mv3pose` and `mv3pose_sliced` have the same `weight.bin`), and the output matches the
+  original PyTorch model (mean depth difference 0.03–0.05% on a Mac).
+- **Head rewrite** (`headcombo`): the DPT depth head uses transposed-convolution upsampling and upsamples later in
+  the head. Depth and confidence change slightly, mostly at object edges. The pose output does not change (the
+  camera decoder reads the backbone, not the head).
+- **392 input**: fewer tokens per image. With the poses from ARKit, the metric scale estimated from the 392
+  variants differs from the 504 sliced variant by a median of 2.6% (3 views) and 4.5% (2 views).
+
+## Measured on iPhone
+
+Two devices only: iPhone 12 Pro (A14, iOS 27.0.1) and iPhone 17 Pro (A19 Pro, iOS 27.0.1), default compute units
+(`.all`). Times are the median inference time of one forward pass (all views). The conditions differ between rows,
+so compare numbers within a row.
+
+| Comparison | Device | Condition | Result |
+|---|---|---|---|
+| `mv3pose` → `mv3pose_sliced` | 17 Pro | Release, ARKit poses, handheld, 120 s each from nominal thermal state | 464.3 → 310.5 ms, app memory 1,121 → 762 MB |
+| `mv3pose` → `mv3pose_sliced` | 12 Pro | Release, ARKit poses, handheld, 1 min each, not cooled in between | 774 → 566 ms, Neural Engine memory 760 → 367 MB |
+| `mv3pose_sliced` → `mv3pose392_sliced8_headcombo` | 17 Pro | Debug build test, fixed images | 224 → 91 ms |
+| `mv3pose_sliced` → `mv3pose392_sliced8_headcombo` | 12 Pro | Debug build test, fixed images | 579.5 → 183.4 ms |
+| `mv2pose392_sliced_headcombo` | 17 Pro | Debug build test, fixed images | 53.6–54.6 ms |
+
+Notes from these devices:
+
+- On the iPhone 17 Pro Neural Engine, the 2-view 504 sliced package does not return (the first inference stalls
+  and is cancelled). The 2-view 392 package above returns.
+- A 3-view 392 package with 16 slices also does not return on the iPhone 17 Pro, which is why the recommended
+  3-view variant uses 8 slices.
+- The first load compiles the model for the Neural Engine and can take one to several minutes on an iPhone 12 Pro.
+
+## Inputs
+
+| Name | Type | Shape | Content |
+|---|---|---|---|
+| `image_0` … `image_{S-1}` | Image (RGB, 8-bit) | 392 × 392 or 504 × 504 | The views. View 0 is the reference view. ImageNet normalisation is inside the model. |
+| `extrinsics` | MultiArray Float32 | (1, S, 4, 4) | World-to-camera matrices in the OpenCV camera convention (x right, y down, z forward), **already normalised** (see below). |
+| `intrinsics` | MultiArray Float32 | (1, S, 3, 3) | Pinhole intrinsics of each model input image in pixels (after cropping and resizing to the input size). Only `fx` and `fy` reach the network. |
+
+The normalisation of the extrinsics is the one the official `DepthAnything3._normalize_extrinsics` (`api.py`) does
+before the forward pass. It is left to the caller so that it runs in Float32 / Float64 instead of the FP16 of the
+model:
+
+```python
+import numpy as np
+
+def normalize_extrinsics(w2c):            # w2c: (S, 4, 4) world-to-camera, OpenCV axes, metres
+    rel = w2c @ np.linalg.inv(w2c[0])     # view 0 becomes the identity
+    centres = np.linalg.inv(rel)[:, :3, 3]
+    dist = np.sort(np.linalg.norm(centres, axis=1))
+    median = max(dist[(len(dist) - 1) // 2], 0.1)   # torch.median: the lower middle value
+    rel[:, :3, 3] /= median
+    return rel.astype(np.float32), median
+```
+
+With 2 views the median is the distance of view 0 to itself, so the divisor is always 0.1.
+
+ARKit's camera axes (`ARCamera.transform`) are x right, y up and z toward the viewer in the sensor's landscape
+orientation, so the camera-to-world from ARKit has to be rotated into the OpenCV axes of the image you pass before it
+is inverted to world-to-camera. For an image rotated 90 degrees clockwise to portrait (the usual way to show it
+upright when the phone is held in portrait), the model's x is ARKit's +y, the model's y is ARKit's +x and the
+model's z is ARKit's −z: `c2w_model = c2w_arkit @ R`, with the columns of `R` being (0, 1, 0), (1, 0, 0) and
+(0, 0, −1). The intrinsics have to follow the same rotation, centre crop and resize.
+
+## Outputs
+
+| Name | Type | Shape | Content |
+|---|---|---|---|
+| `depth` | MultiArray Float16 | (1, S, H, W) | Depth of every view in the normalised frame of the input poses. |
+| `confidence` | MultiArray Float16 | (1, S, H, W) | Confidence of every depth value. |
+| `pose_encoding` | MultiArray Float16 | (1, S, 9) | The pose the network estimates for every view: translation (3), quaternion xyzw (4), horizontal and vertical field of view. The translation is the camera position (camera-to-world) in the normalised frame. |
+
+To bring the depth to metres, do what the official `api.py` does: align the estimated camera positions from
+`pose_encoding` to the input camera positions with Umeyama's method (similarity transform) and divide the depth by
+the resulting scale.
+
+## Model list
+
+`models.json` lists every package with the size and SHA-256 of each file inside it, the input size, the number of
+views and the minimum iOS version. Apps can read it to download the files of a package one by one from
+`https://huggingface.co/shu223/Depth-Anything-3-Small-MultiView-CoreML/resolve/main/<path>`, check them and compile
+the package on the device with `MLModel.compileModel(at:)`.
+
+## Conversion
+
+Converted with coremltools from the original PyTorch implementation
+([ByteDance-Seed/Depth-Anything-3](https://github.com/ByteDance-Seed/Depth-Anything-3)), with the changes listed in
+`NOTICE`. Ray decoding, sky handling and the Gaussian branch of the original model are not included.
+
+## License
+
+Apache License 2.0, the same as the original weights. See `LICENSE` and `NOTICE`.
+
+## Citation
+
+```bibtex
+@article{depthanything3,
+  title   = {Depth Anything 3: Recovering the Visual Space from Any Views},
+  author  = {Lin, Haotong and Chen, Sili and Liew, Jun Hao and Chen, Donny Y. and Li, Zhenyu and Shi, Guang and Feng, Jiashi and Kang, Bingyi},
+  journal = {arXiv preprint arXiv:2511.10647},
+  year    = {2025}
+}
+```

models.json

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+{
+  "schema": 1,
+  "models": [
+    {
+      "id": "mv3pose392_sliced8_headcombo",
+      "title": "Depth Anything 3 Small (3 views, 392, sliced 8, headcombo)",
+      "path": "models/mv3pose392_sliced8_headcombo.mlpackage",
+      "files": [
+        {
+          "path": "Data/com.apple.CoreML/model.mlmodel",
+          "bytes": 460938,
+          "sha256": "5859d75479693098baa075fb0b4a853ef933b4795934f6a210254d01de5fc783"
+        },
+        {
+          "path": "Data/com.apple.CoreML/weights/weight.bin",
+          "bytes": 71536576,
+          "sha256": "d3a040837c465c18c862c550c2afa907797212df1baeeb4bc2abd85f53501b67"
+        },
+        {
+          "path": "Manifest.json",
+          "bytes": 617,
+          "sha256": "7c34f36dd2fb6a4d7b1532e9c971cb775d39739e1ce255f9805e6c823d345c1c"
+        }
+      ],
+      "inputSize": 392,
+      "viewCount": 3,
+      "requiresPoses": true,
+      "minimumOS": "18.0",
+      "recommended": true,
+      "notes": "All speed-ups combined: input 392, global attention sliced into 8, rewritten head. The smallest and fastest of the 3-view models; runs on iPhone 12 Pro and 17 Pro."
+    },
+    {
+      "id": "mv2pose392_sliced_headcombo",
+      "title": "Depth Anything 3 Small (2 views, 392, sliced, headcombo)",
+      "path": "models/mv2pose392_sliced_headcombo.mlpackage",
+      "files": [
+        {
+          "path": "Data/com.apple.CoreML/model.mlmodel",
+          "bytes": 538893,
+          "sha256": "3159ef4b501d25e5db0b1cb866b9ede900de613883e359fb8ad51084ae6d130d"
+        },
+        {
+          "path": "Data/com.apple.CoreML/weights/weight.bin",
+          "bytes": 71234240,
+          "sha256": "7c1ed360296ef62c13fde09c5bb3a46d96b3681cb3802c8c50a5dbdfb3b929e4"
+        },
+        {
+          "path": "Manifest.json",
+          "bytes": 617,
+          "sha256": "8a7196b1e1e0afb45540a35fb4cf85e34aede2d3978f34257bb607804f8eb2a2"
+        }
+      ],
+      "inputSize": 392,
+      "viewCount": 2,
+      "requiresPoses": true,
+      "minimumOS": "18.0",
+      "recommended": false,
+      "notes": "The same speed-ups with 2 views."
+    },
+    {
+      "id": "mv3pose_sliced",
+      "title": "Depth Anything 3 Small (3 views, 504, sliced)",
+      "path": "models/mv3pose_sliced.mlpackage",
+      "files": [
+        {
+          "path": "Data/com.apple.CoreML/model.mlmodel",
+          "bytes": 535887,
+          "sha256": "df7f236d172382c224c0f056f1bbb40b9daca6b16e118fecb2a57c556eb41a02"
+        },
+        {
+          "path": "Data/com.apple.CoreML/weights/weight.bin",
+          "bytes": 86300544,
+          "sha256": "1ee056122b5b01690b5e61e20d562e4d2d61cad9d96373e2c0e7efde3f667a3f"
+        },
+        {
+          "path": "Manifest.json",
+          "bytes": 617,
+          "sha256": "1de3b678d2a12257ccf4c4cfd9e5716bc31ae63d4279c1319d4c25c894710090"
+        }
+      ],
+      "inputSize": 504,
+      "viewCount": 3,
+      "requiresPoses": true,
+      "minimumOS": "18.0",
+      "recommended": false,
+      "notes": "Input 504 with the global attention sliced."
+    },
+    {
+      "id": "mv3pose",
+      "title": "Depth Anything 3 Small (3 views, 504)",
+      "path": "models/mv3pose.mlpackage",
+      "files": [
+        {
+          "path": "Data/com.apple.CoreML/model.mlmodel",
+          "bytes": 398101,
+          "sha256": "4bfb7097f81a26e48a3ade77b81b80dbc16135b56cba5daffe1b2dd2a7993fdd"
+        },
+        {
+          "path": "Data/com.apple.CoreML/weights/weight.bin",
+          "bytes": 86300544,
+          "sha256": "1ee056122b5b01690b5e61e20d562e4d2d61cad9d96373e2c0e7efde3f667a3f"
+        },
+        {
+          "path": "Manifest.json",
+          "bytes": 617,
+          "sha256": "96dad3c07dcd28683a674c4f1c362d42aa4297db25aebdc21240eb05ef28287b"
+        }
+      ],
+      "inputSize": 504,
+      "viewCount": 3,
+      "requiresPoses": true,
+      "minimumOS": "17.0",
+      "recommended": false,
+      "notes": "Input 504, converted without changes to the attention or the head."
+    }
+  ]
+}