Small Python utility to compare and visualize the output of various stereo reconstruction algorithms:

• Make it easy to get a qualitative evaluation of several state-of-the-art models in the wild
• Feed it left/right images or capture live from an OAK-D camera
• Interactive colored point-cloud view since nice-looking disparity images can be misleading
• Try different parameters on the same image

Supported methods (implementation/pre-trained models taken from their respective authors):

• OpenCV stereo block matching and Semi-global block matching baselines, with all their parameters
• CREStereo: Practical Stereo Matching via Cascaded Recurrent Network with Adaptive Correlation (CVPR 2022)
• RAFT-Stereo: "Multilevel Recurrent Field Transforms for Stereo Matching." (3DV 2021)
• Hitnet: "Hierarchical Iterative Tile Refinement Network for Real-time Stereo Matching." (CVPR 2021)
• Chang et al. RealtimeStereo: "Attention-Aware Feature Aggregation for Real-time Stereo Matching on Edge Devices" (ACCV 2020)

See below for more details / credits to get each of these working.

python3 -m pip install stereodemo

To capture data directly from an OAK-D camera, use:

stereodemo --oak

Then click on Next Image to capture a new one.

For convenience a tiny subset of some popular datasets is included in this repository. Just provide a folder to stereodemo and it'll look for left/right pairs (either im0/im1 or left/right in the names):

# To evaluate on the oak-d images
stereodemo datasets/oak-d 

# To cycle through all images
stereodemo datasets

Then click on Next Image to cycle through the images.

Sample images included in this repository:

• drivingstereo: outdoor driving.
• middlebury_2014: high-res objects.
• eth3d: outdoor and indoor scenes.
• sceneflow: synthetic rendering of objects.
• oak-d: indoor images I captured with my OAK-D lite camera.

pip will install the dependencies automatically. Here is the list:

• Open3D. For the point cloud visualization and the GUI.
• OpenCV. For image loading and the traditional block matching baselines.
• onnxruntime. To run pretrained models in the ONNX format.
• pytorch. To run pretrained models exported as torch script.
• depthai. Optional, to grab images from a Luxonis OAK camera.

I did not implement any of these myself, but just collected pre-trained models or converted them to torch script / ONNX.

• CREStereo

  • Official implementation and pre-trained models: https://github.com/megvii-research/CREStereo
  • Model Zoo for the ONNX models: https://github.com/PINTO0309/PINTO_model_zoo/tree/main/284_CREStereo
  • Port to ONNX + sample loading code: https://github.com/ibaiGorordo/ONNX-CREStereo-Depth-Estimation

• RAFT-Stereo

  • Official implementation and pre-trained models: https://github.com/princeton-vl/RAFT-Stereo
  • I exported the pytorch implementation to torch script via tracing, with minor modifications of the source code.
  • Their fastest implementation was not imported.

• Hitnet

  • Official implementation and pre-trained models: https://github.com/google-research/google-research/tree/master/hitnet
  • Model Zoo for the ONNX models: https://github.com/PINTO0309/PINTO_model_zoo/tree/main/142_HITNET
  • Port to ONNX + sample loading code: https://github.com/ibaiGorordo/ONNX-HITNET-Stereo-Depth-estimation

• Chang et al. RealtimeStereo

  • Official implementation and pre-trained models: https://github.com/JiaRenChang/RealtimeStereo
  • I exported the pytorch implementation to torch script via tracing with some minor changes to the code JiaRenChang/RealtimeStereo#15 . See chang_realtimestereo_to_torchscript_onnx.py.

The code of steredemo is MIT licensed, but the pre-trained models are subject to the license of their respective implementation.

The sample images have the license of their respective source, except for datasets/oak-d which is licenced under Creative Commons Attribution 4.0 International License.