Dmytro Mishkin presents Image Matching across Wide Baselines: From Paper to Practice

We introduce a comprehensive benchmark for local features and robust estimation
algorithms, focusing on the downstream task -- the accuracy of the reconstructed
camera pose -- as our primary metric. Our pipeline's modular structure allows
easy integration, configuration, and combination of different methods and
heuristics. This is demonstrated by embedding dozens of popular algorithms and
evaluating them, from seminal works to the cutting edge of machine learning
research. We show that with proper settings, classical solutions may still
outperform the perceived state of the art.
Besides establishing the actual state of the art, the conducted experiments
reveal unexpected properties of Structure from Motion (SfM) pipelines that can
help improve their performance, for both algorithmic and learned methods. Data
and code are online (https://github.com/vcg-uvic/image-matching-benchmark),
providing an easy-to-use and flexible framework for the benchmarking of local
features and robust estimation methods, both alongside and against
top-performing methods. This work provides a basis for the Image Matching
Challenge (https://vision.uvic.ca/image-matching-challenge/)

The seminar will be via Zoom https://feectu.zoom.us/j/99016161302