OlafenwaMoses · OlafenwaMoses · May 4, 2026 · May 4, 2026 · May 4, 2026 · May 4, 2026
diff --git a/docs/hardware_acceleration.md b/docs/hardware_acceleration.md
@@ -0,0 +1,109 @@
+# Hardware Acceleration
+
+vizion3d detects the best available device automatically at runtime — no code changes required. Choose the install extra that matches your hardware.
+
+| Backend | Hardware | Platforms | What drives inference |
+|---|---|---|---|
+| **CPU** | Any processor | Linux, Windows, macOS | PyTorch CPU kernels |
+| **CUDA** | NVIDIA GPU (Kepler+) | Linux, Windows | CUDA cores / Tensor Cores (Ampere+) |
+| **MPS** | Apple Silicon (M1 / M2 / M3 / M4) | macOS 12.3+ | Metal GPU via unified memory |
+| **ROCm** | AMD GPU (RDNA2+, CDNA, CDNA2, CDNA3) | **Linux only** | ROCm HIP runtime |
+
+---
+
+## CPU (default)
+
+Works on every platform with no additional drivers. **This is the recommended install for most users.** Inference runs on PyTorch's CPU backend and automatically upgrades to NVIDIA CUDA or Apple Silicon MPS if detected at runtime — no separate install needed for those.
+
+**pip**
+```bash
+pip install "vizion3d[cpu]"
+```
+
+**uv**
+```bash
+uv add "vizion3d[cpu]"
+```
+
+> **Mac M-series users:** the standard CPU extra automatically includes Metal Performance Shaders (MPS) support — no separate install needed. vizion3d will use your GPU via MPS as long as you are on macOS 12.3 or later with PyTorch ≥ 2.0.
+
+---
+
+## NVIDIA CUDA
+
+Delivers the highest throughput for depth estimation. On NVIDIA Ampere GPUs and newer (RTX 30xx / A100 and above), PyTorch additionally uses Tensor Cores for mixed-precision acceleration.
+
+### Prerequisites
+
+| Requirement | Minimum version | Link |
+|---|---|---|
+| NVIDIA GPU driver | 520.61.05 (Linux) / 528.33 (Windows) | [Driver downloads](https://www.nvidia.com/drivers) |
+| CUDA Toolkit | 11.8 | [CUDA Toolkit installer](https://developer.nvidia.com/cuda-downloads) |
+| cuDNN | 8.x | [cuDNN install guide](https://developer.nvidia.com/cudnn) |
+
+Install CUDA and cuDNN **before** installing vizion3d. The PyTorch wheel bundled with the `cuda` extra already includes its own CUDA runtime libraries, but the driver must be present on the host.
+
+**pip**
+```bash
+pip install "vizion3d[cuda]"
+```
+
+**uv**
+```bash
+uv add "vizion3d[cuda]"
+```
+
+vizion3d detects CUDA via `torch.cuda.is_available()` at runtime and moves models and tensors to the GPU automatically — no configuration needed.
+
+---
+
+## AMD ROCm
+
+Provides GPU-accelerated inference on supported AMD GPUs using the ROCm open-source compute stack. ROCm exposes itself through PyTorch's CUDA namespace (`torch.cuda.is_available()` returns `True`), so vizion3d uses it transparently with no code changes.
+
+> **Platform:** ROCm is supported on **Linux only**. There is no ROCm support for Windows or macOS.
+
+### Supported hardware
+
+| Family | Examples |
+|---|---|
+| RDNA2 | RX 6700 XT, RX 6800, RX 6900 XT |
+| RDNA3 | RX 7800 XT, RX 7900 XTX |
+| CDNA | Instinct MI100 |
+| CDNA2 | Instinct MI200 series |
+| CDNA3 | Instinct MI300 series |
+
+For the full supported GPU list see the [AMD ROCm hardware compatibility guide](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/reference/system-requirements.html).
+
+### Prerequisites
+
+Install the ROCm stack on your system before installing the PyTorch ROCm wheel. Follow AMD's official guide:
+
+- [ROCm installation for Linux](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/)
+
+### Install
+
+Because the ROCm PyTorch wheel is hosted on PyTorch's own index (not PyPI), it must be installed **before** vizion3d — vizion3d's base install has no torch dependency and will not overwrite it.
+
+**Step 1 — install ROCm PyTorch**
+```bash
+pip3 install --pre torch torchvision torchaudio \
+  --index-url https://download.pytorch.org/whl/nightly/rocm7.2
+```
+
+For the full list of available ROCm wheel versions see [PyTorch ROCm install guide](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/pytorch-install.html#using-wheels-package).
+
+**Step 2 — install vizion3d (no extra needed)**
+```bash
+pip install vizion3d
+```
+
+Because `vizion3d` declares no torch dependency in its base install, pip will not touch the ROCm wheel you installed in step 1.
+
+> **Warning:** do **not** run `pip install "vizion3d[cpu]"` or `pip install "vizion3d[cuda]"` after installing the ROCm wheel — those extras pull a standard PyPI torch build and will replace your ROCm installation.
+
+### Limitations
+
+- Linux only — ROCm does not run on Windows or macOS.
+- Only GPUs on AMD's official support list are guaranteed to work; consumer RDNA1 cards (RX 5000 series) are not supported.
+- Some PyTorch operations fall back to CPU on ROCm; performance for those ops will match CPU speed.
diff --git a/docs/index.md b/docs/index.md
@@ -16,7 +16,7 @@ Every task is accessible through three consumption modes driven by one shared CQ
 
 Requires **Python 3.12** (Open3D constraint).
 
-PyTorch is **not bundled** in the base install — choose the extra that matches your hardware (see [Hardware acceleration](#hardware-acceleration) below). For most users the `cpu` extra is the right default; it installs the standard PyTorch wheel which covers CPU, NVIDIA CUDA, and Apple Silicon MPS automatically.
+PyTorch is **not bundled** in the base install — choose the extra that matches your hardware (see [Hardware Acceleration](hardware_acceleration.md)). For most users the `cpu` extra is the right default; it installs the standard PyTorch wheel which covers CPU, NVIDIA CUDA, and Apple Silicon MPS automatically.
 
 **pip**
 ```bash 
@@ -38,113 +38,9 @@ uv add "vizion3d[cpu]"
 
 ## Hardware acceleration
 
-vizion3d detects the best available device automatically at runtime — no code changes required. Choose the install extra that matches your hardware.
+vizion3d detects the best available backend automatically at runtime — no code changes required. Supported backends are CPU, NVIDIA CUDA, Apple Silicon MPS, and AMD ROCm.
 
-| Backend | Hardware | Platforms | What drives inference |
-|---|---|---|---|
-| **CPU** | Any processor | Linux, Windows, macOS | PyTorch CPU kernels |
-| **CUDA** | NVIDIA GPU (Kepler+) | Linux, Windows | CUDA cores / Tensor Cores (Ampere+) |
-| **MPS** | Apple Silicon (M1 / M2 / M3 / M4) | macOS 12.3+ | Metal GPU via unified memory |
-| **ROCm** | AMD GPU (RDNA2+, CDNA, CDNA2, CDNA3) | **Linux only** | ROCm HIP runtime |
-
----
-
-### CPU (default)
-
-Works on every platform with no additional drivers. **This is the recommended install for most users.** Inference runs on PyTorch's CPU backend and automatically upgrades to NVIDIA CUDA or Apple Silicon MPS if detected at runtime — no separate install needed for those.
-
-**pip**
-```bash
-pip install "vizion3d[cpu]"
-```
-
-**uv**
-```bash
-uv add "vizion3d[cpu]"
-```
-
-> **Mac M-series users:** the standard CPU extra automatically includes Metal Performance Shaders (MPS) support — no separate install needed. vizion3d will use your GPU via MPS as long as you are on macOS 12.3 or later with PyTorch ≥ 2.0.
-
----
-
-### NVIDIA CUDA
-
-Delivers the highest throughput for depth estimation. On NVIDIA Ampere GPUs and newer (RTX 30xx / A100 and above), PyTorch additionally uses Tensor Cores for mixed-precision acceleration.
-
-#### Prerequisites
-
-| Requirement | Minimum version | Link |
-|---|---|---|
-| NVIDIA GPU driver | 520.61.05 (Linux) / 528.33 (Windows) | [Driver downloads](https://www.nvidia.com/drivers) |
-| CUDA Toolkit | 11.8 | [CUDA Toolkit installer](https://developer.nvidia.com/cuda-downloads) |
-| cuDNN | 8.x | [cuDNN install guide](https://developer.nvidia.com/cudnn) |
-
-Install CUDA and cuDNN **before** installing vizion3d. The PyTorch wheel bundled with the `cuda` extra already includes its own CUDA runtime libraries, but the driver must be present on the host.
-
-**pip**
-```bash
-pip install "vizion3d[cuda]"
-```
-
-**uv**
-```bash
-uv add "vizion3d[cuda]"
-```
-
-vizion3d detects CUDA via `torch.cuda.is_available()` at runtime and moves models and tensors to the GPU automatically — no configuration needed.
-
----
-
-### AMD ROCm
-
-Provides GPU-accelerated inference on supported AMD GPUs using the ROCm open-source compute stack. ROCm exposes itself through PyTorch's CUDA namespace (`torch.cuda.is_available()` returns `True`), so vizion3d uses it transparently with no code changes.
-
-> **Platform:** ROCm is supported on **Linux only**. There is no ROCm support for Windows or macOS.
-
-#### Supported hardware
-
-| Family | Examples |
-|---|---|
-| RDNA2 | RX 6700 XT, RX 6800, RX 6900 XT |
-| RDNA3 | RX 7800 XT, RX 7900 XTX |
-| CDNA | Instinct MI100 |
-| CDNA2 | Instinct MI200 series |
-| CDNA3 | Instinct MI300 series |
-
-For the full supported GPU list see the [AMD ROCm hardware compatibility guide](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/reference/system-requirements.html).
-
-#### Prerequisites
-
-Install the ROCm stack on your system before installing the PyTorch ROCm wheel. Follow AMD's official guide:
-
-- [ROCm installation for Linux](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/)
-
-#### Install
-
-Because the ROCm PyTorch wheel is hosted on PyTorch's own index (not PyPI), it must be installed **before** vizion3d — vizion3d's base install has no torch dependency and will not overwrite it.
-
-**Step 1 — install ROCm PyTorch**
-```bash
-pip3 install --pre torch torchvision torchaudio \
-  --index-url https://download.pytorch.org/whl/nightly/rocm7.2
-```
-
-For the full list of available ROCm wheel versions see [PyTorch ROCm install guide](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/pytorch-install.html#using-wheels-package).
-
-**Step 2 — install vizion3d (no extra needed)**
-```bash
-pip install vizion3d
-```
-
-Because `vizion3d` declares no torch dependency in its base install, pip will not touch the ROCm wheel you installed in step 1.
-
-> **Warning:** do **not** run `pip install "vizion3d[cpu]"` or `pip install "vizion3d[cuda]"` after installing the ROCm wheel — those extras pull a standard PyPI torch build and will replace your ROCm installation.
-
-#### Limitations
-
-- Linux only — ROCm does not run on Windows or macOS.
-- Only GPUs on AMD's official support list are guaranteed to work; consumer RDNA1 cards (RX 5000 series) are not supported.
-- Some PyTorch operations fall back to CPU on ROCm; performance for those ops will match CPU speed.
+For per-backend prerequisites, install commands, and platform notes, see the [Hardware Acceleration](hardware_acceleration.md) page.
 
 ---
 
@@ -215,4 +111,5 @@ Each task lives in its own module under `vizion3d/<category>/` and exposes exact
 | Task | Status | Docs |
 |---|---|---|
 | Monocular depth estimation | Stable | [Depth Estimation](features/depth_estimation.md) |
+| Stereo depth estimation | Stable | [Stereo Depth](features/stereo_depth.md) |
 
diff --git a/mkdocs.yml b/mkdocs.yml
@@ -24,10 +24,13 @@ plugins:
 
 nav:
   - Home: index.md
+  - Hardware Acceleration: hardware_acceleration.md
   - Features:
-      - Depth Estimation: features/depth_estimation.md
-      - Depth Estimation Advanced Config: features/depth_estimation_advanced_config.md
-      - Stereo Depth: features/stereo_depth.md
+      - Depth Estimation:
+          - Overview: features/depth_estimation.md
+          - Advanced Config: features/depth_estimation_advanced_config.md
+      - Stereo Depth:
+          - Overview: features/stereo_depth.md
       - Depth Estimation vs Stereo Depth: features/depth_estimation_vs_stereo_depth.md
   - API Reference:
       - Lifting (2D → 3D): api/lifting.md