Stereo Vision in ADAS: Depth Perception Beyond LiDAR
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In this video, we cover:
~ The limitations of 3D LiDAR systems and how stereo vision can be used to extract 3D information for ADAS.
~ An introduction to stereo vision and training the stereo transformer model.
~ Capturing images from two slightly different viewpoints to calculate the disparity map.
~ The advantages of stereo vision cameras over LiDAR, including high-resolution image capture and cost-effectiveness.
~ Using the KITTI Stereo 2015 dataset to train our model for disparity and occlusion prediction.
~ The architecture of the STTR (Stereo Transformer) model and its efficient global context acquisition.
~ Steps to prepare the dataset and train the model, including dataset augmentation, training loop, and model checkpointing.
~ Running inference to generate predicted disparity and occlusion maps.
💡 What You’ll Learn:
~ The workflow for integrating ADAS with stereo vision using the Stereo Transformer model.
~ Visualizing disparity maps and understanding the model architecture.
~ Detailed training procedures, code walkthroughs, and training results using the KITTI Stereo 2015 dataset.
~ Practical applications and benefits of stereo vision in ADAS.
Watch our video on Learn OpenCV to dive into the implementation and training of the Stereo Transformer model for ADAS.
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