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README.md

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+---
+license: mit
+library_name: litert
+pipeline_tag: depth-estimation
+base_model: Ruicheng/moge-2-vits-normal
+tags:
+  - litert
+  - tflite
+  - on-device
+  - android
+  - monocular-geometry
+  - depth-estimation
+  - surface-normals
+  - point-cloud
+  - dinov2
+---
+
+# MoGe-2 ViT-S — LiteRT (TFLite) GPU
+
+On-device [LiteRT](https://ai.google.dev/edge/litert) (`.tflite`) conversion of
+**[MoGe-2](https://github.com/microsoft/MoGe)** (CVPR'25 Oral) monocular geometry
+estimation, converted from [`Ruicheng/moge-2-vits-normal`](https://huggingface.co/Ruicheng/moge-2-vits-normal)
+(DINOv2 ViT-S backbone, 35M params).
+
+A single forward pass turns one RGB image into an **affine 3D point map**,
+**surface normals**, a **confidence mask**, and a **metric scale** — enabling
+depth, surface normals, and a rotatable 3D point cloud on a phone.
+
+The model runs **fully on the LiteRT `CompiledModel` GPU accelerator** (ML Drift):
+all 836 ops are GPU-native, no CPU fallback, no Flex ops.
+
+## Files
+
+| File | Size | Description |
+|------|------|-------------|
+| `moge.tflite` | 136 MB | FP32 single-graph model, GPU-compatible |
+
+## I/O
+
+- **Input**: `[1, 3, 448, 448]` float32, **NCHW**, RGB normalized to `[0, 1]`
+  (ImageNet mean/std is applied *inside* the graph).
+- **Outputs** (4):
+  - `points` `[1, 448, 448, 3]` — affine point map (`exp` remap: `[xy·exp(z), exp(z)]`)
+  - `normal` `[1, 448, 448, 3]` — L2-normalized surface normals
+  - `mask`   `[1, 448, 448, 1]` — sigmoid confidence (> 0.5 = valid)
+  - `scale`  `[1, 1, 1, 1]`     — metric scale factor
+
+## Usage (Android, LiteRT CompiledModel)
+
+```kotlin
+val model = CompiledModel.create(
+    context.assets, "moge.tflite",
+    CompiledModel.Options(Accelerator.GPU), null
+)
+val inputs = model.createInputBuffers()
+val outputs = model.createOutputBuffers()
+inputs[0].writeFloat(nchwFloatArray)   // [1,3,448,448], RGB [0,1]
+model.run(inputs, outputs)
+val points = outputs[0].readFloat()    // identify the 4 outputs by element count + range
+```
+
+A complete Android sample (gallery → normal map + depth) is available in
+[google-ai-edge/litert-samples](https://github.com/google-ai-edge/litert-samples).
+
+## Performance
+
+- ~522 ms / frame on a Pixel 8a (Mali-G615) GPU.
+
+## Conversion notes
+
+Converted with [litert-torch](https://github.com/google-ai-edge/ai-edge-torch)
+(NCHW preserved — required for ViT attention accuracy). Making DINOv2 + the
+ConvStack decoder fully GPU-compatible required nine graph rewrites
+(LayerScale bake, fused-qkv decomposition, position-embedding bake,
+ConvTranspose → bilinear+1×1, etc.). Verified: all ops GPU-native, output
+correlation ≈ 1.0 vs. the PyTorch reference.
+
+## License & attribution
+
+- Model: **MIT** (original [microsoft/MoGe](https://github.com/microsoft/MoGe/blob/main/LICENSE)).
+- DINOv2 backbone components: Apache-2.0.
+- This is a format conversion of `Ruicheng/moge-2-vits-normal`; all credit to the
+  original authors (Microsoft Research).