The focus of our DNNs is on real-time inference for computer vision and perception. To achieve real-time capabilities, we use multi-tasking deep learning architectures which are sharing common backbones, while inference results are provided via independent network heads (e.g. object detection, scene segmentation, 3D reconstruction, features tracking).
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Simultaneous semantic segmentation, object detection and keypoints tracking using RovisLab's Multi-tasking Deep Neural Network architecture.
Driving scene perception and 3D reconstruction of the road model on a highway.