| --- |
| library_name: onnx |
| license: other |
| tags: |
| - foundation |
| - amd |
| - rocm |
| - depth-estimation |
| pipeline_tag: depth-estimation |
| --- |
| |
|  |
|
|
| # Bi3D: Optimized for AMD ROCm |
|
|
| Bi3D performs stereo depth estimation by reformulating disparity search as a sequence of binary classifications over a cost volume, with optional 3D regularization for sub-pixel continuous depth. This repository packages evaluation/inference for stereo depth / disparity estimation using ONNX Runtime, MIGraphX EP, and VitisAI EP, exported and validated for **AMD ROCm** so it runs efficiently on AMD GPUs, CPUs, and NPUs. |
|
|
| This is based on the implementation of Bi3D found [here](https://github.com/NVlabs/Bi3D). |
| This repository contains configurations and scripts optimized for **AMD® ROCm™** platforms. You can use the [Bi3D AMD scripts](https://github.com/AMD-PAVS/physical_ai_sdk/blob/main/models/bi3d) to reproduce results or export with custom configurations. |
|
|
| --- |
|
|
| ## Task Overview |
|
|
| **Task:** Stereo depth / disparity estimation |
|
|
| **Dataset:** SceneFlow FlyingThings3D TEST (cleanpass WebP; bundled 10-pair subset committed in-repo) |
|
|
| **Output metrics:** EPE (End-Point Error, mean absolute disparity error in pixels) |
|
|
| > **NPU note:** Uses VitisAI EP auto-partitioning with per-dtype `config/vitisai_config_*.json` files. |
| |
| --- |
| |
| ## AMD ROCm Optimization |
| |
| This model export has been adapted and validated for **AMD Instinct™ / Radeon™ GPUs** running **ROCm**, as well as AMD CPUs and NPUs. Key points: |
| |
| - Validated backends: **ONNX Runtime** (CPU), **MIGraphX EP** (GPU), and **VitisAI EP** (NPU). |
| |
| | Runtime | Precision | Backend | Hardware | Notes | |
| |---|---|---|---|---| |
| | CPU | FP32 / FP16 / BF16 | ONNX Runtime | AMD RYZEN AI MAX+ 395 w/ Radeon 8060S | — | |
| | GPU | FP32 / FP16 / BF16 | MIGraphX EP | AMD RYZEN AI MAX+ 395 w/ Radeon 8060S | — | |
| | NPU | FP32 / FP16 / BF16 | VitisAI EP | AMD RYZEN AI MAX+ 395 w/ Radeon 8060S | Auto-partitioning via per-dtype `config/vitisai_config_*.json` | |
|
|
| --- |
|
|
| ## Getting Started |
|
|
| For setup instructions, evaluation scripts, and custom configuration options, see the [Bi3D on GitHub](https://github.com/AMD-PAVS/physical_ai_sdk/blob/main/models/bi3d). |
|
|
| --- |
|
|
| ## Model Details |
|
|
| **Model Type:** Stereo depth estimation (binary classification over a disparity cost volume, with 3D regularization for continuous depth) |
|
|
| **Base Model:** SceneFlow-trained continuous depth 3D + confidence regularization checkpoint (NVIDIA Bi3D) |
|
|
| **Model Stats:** |
| - Model variant: continuous depth 3D + confidence regularization (default checkpoint) |
| - Input resolution: 288×480 (cropped from the original 540×960 to fit device memory constraints) |
| - Precision tested: FP32, FP16, BF16 |
|
|
| --- |
|
|
| ## Dig Deeper |
|
|
| Want to explore the full evaluation scripts, config options, and other AMD-optimized model examples? |
|
|
| 📂 **[View the full project on GitHub](https://github.com/AMD-PAVS/physical_ai_sdk/blob/main/models/bi3d)** |
|
|
| The GitHub repository includes: |
| - Setup and prerequisites for ROCm environments |
| - Scripts for the supported runners |
| - Additional model variants and datasets |
| - Benchmarking and reproduction instructions |
|
|
