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library_name: pytorch
---
PSCVStereo is a lightweight end-to-end neural stereo matching model inspired by RAFT-Stereo, IGEV-Stereo and CREStereo, optimized for deployment on Ambarella CVflow hardware (CV7, CV72, and N1-655). It estimates pixel-wise disparity from left and right camera images for depth perception on robotics and autonomous mobile robots (AMRs).
- **Technical**: Uses a lightweight correlation cost volume and iterative refinement, with CVflow-aware optimizations and sparsification.
- **Applications**: Depth estimation, obstacle detection, and navigation for edge robotics and AMRs.
- **Output**: Disparity map and confidence mask for confidence-aware depth perception.
**Related works**:
[RAFT-Stereo: Multilevel Recurrent Field Transforms for Stereo Matching](https://arxiv.org/abs/2109.07547)
[Iterative Geometry Encoding Volume for Stereo Matching (IGEV-Stereo)](https://arxiv.org/abs/2303.06615)
[Practical Stereo Matching via Cascaded Recurrent Network (CREStereo)](https://arxiv.org/abs/2203.11483)
**Reference implementation**:
[RAFT-Stereo GitHub repository](https://github.com/princeton-vl/RAFT-Stereo)
[IGEV-Stereo GitHub repository](https://github.com/gangweiX/IGEV)
[CREStereo GitHub repository](https://github.com/megvii-research/CREStereo)
# PSCVStereo-base
This model is the higher-capacity model. It uses a full-width feature backbone and a richer correlation volume, refined with more coarse-to-fine updates over a wider disparity range. It is trained on 512×896, so it is the choice when accuracy on larger images matters most.
Model Configuration:
- Implementation: [PSCVStereo](https://github.com/Ambarella-Inc/cooper-pscvstereo)
- Resolution: 3x512x896
- Support Cooper version:
- Cooper SDK: [3.0.0]
- Cooper Foundry: [2.3]
| Model | Device | compression | Model Link |
| :-----: | :-----: | :-----: | ------- |
| PSCVStereo-base | N1-655 | Amba_optimized | [Model_Link](https://huggingface.co/Ambarella/PSCVStereo/blob/main/n1-655_pscvstereo_base_amba_optimized.bin) |
| PSCVStereo-base | CV7 | Amba_optimized | [Model_Link](https://huggingface.co/Ambarella/PSCVStereo/blob/main/cv7_pscvstereo_base_amba_optimized.bin) |
| PSCVStereo-base | CV72 | Amba_optimized | [Model_Link](https://huggingface.co/Ambarella/PSCVStereo/blob/main/cv72_pscvstereo_base_amba_optimized.bin) |
# PSCVStereo-tiny
This model is a more compact variant. It uses a narrower backbone and a slimmer correlation volume, with fewer iterative updates. It is trained on 416×576, so it is the choice when lower compute cost and faster inference are the priority.
Model Configuration:
- Implementation: [PSCVStereo](https://github.com/Ambarella-Inc/cooper-pscvstereo)
- Resolution: 3x416x576
- Support Cooper version:
- Cooper SDK: [3.0.0]
- Cooper Foundry: [2.3]
| Model | Device | compression | Model Link |
| :-----: | :-----: | :-----: | ------- |
| PSCVStereo-tiny | N1-655 | Amba_optimized | [Model_Link](https://huggingface.co/Ambarella/PSCVStereo/blob/main/n1-655_pscvstereo_tiny_amba_optimized.bin) |
| PSCVStereo-tiny | CV7 | Amba_optimized | [Model_Link](https://huggingface.co/Ambarella/PSCVStereo/blob/main/cv7_pscvstereo_tiny_amba_optimized.bin) |
| PSCVStereo-tiny | CV72 | Amba_optimized | [Model_Link](https://huggingface.co/Ambarella/PSCVStereo/blob/main/cv72_pscvstereo_tiny_amba_optimized.bin) |
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