Fabio Tosi

Awesome-Deep-Stereo-Matching. A curated list of awesome Deep Stereo Matching resources

NeRF-Supervised-Deep-Stereo. A novel paradigm for collecting and generating stereo training data using neural rendering

ZipDepth. [ECCV 2026] Official implementation of "ZipDepth: Bringing Lightweight Zero-Shot Monocular Depth Anywhere, on Any Device". A compact 6.1M-parameter network for zero-shot monocular depth estimation, running in real time from server GPUs to mobile phones via knowledge distillation from foundation models.

SMD-Nets. SMD-Nets: Stereo Mixture Density Networks

monoResMatch-Tensorflow. Tensorflow implementation of monocular Residual Matching (monoResMatch) network.

Diffusion4RobustDepth. [ECCV 2024] Diffusion Models for Monocular Depth Estimation: Overcoming Challenging Conditions

CCNN-Tensorflow. Learning from scratch a confidence measure

Unsupervised-Confidence-Measures. This strategy provides labels for training confidence measures based on machine-learning technique without ground-truth labels (BMVC 2017)

LGC-Tensorflow. We propose to exploit nearby and farther clues available from image and disparity domains to obtain a more accurate confidence estimation. While local information is very effective for detecting high frequency patterns, it lacks insights from farther regions in the scene. On the other hand, enlarging the receptive field allows to include clues from farther regions but produces smoother uncertainty estimation, not particularly accurate when dealing with high frequency patterns. For these reasons, we propose a multi-stage cascaded network to combine the best of the two worlds.

Optical-Tracking-Velocimetry. C++