mlsanityData to 7 Oct 2026

End to End Trainable Active Contours via Differentiable Rendering

arXiv 1912.00367 ↗First posted 1 Dec 2019cs.CVcited by 15

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Abstract (arXiv)

We present an image segmentation method that iteratively evolves a polygon. At each iteration, the vertices of the polygon are displaced based on the local value of a 2D shift map that is inferred from the input image via an encoder-decoder architecture. The main training loss that is used is the difference between the polygon shape and the ground truth segmentation mask. The network employs a neural renderer to create the polygon from its vertices, making the process fully differentiable. We demonstrate that our method outperforms the state of the art segmentation networks and deep active contour solutions in a variety of benchmarks, including medical imaging and aerial images. Our code is available at https://github.com/shirgur/ACDRNet.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Dec 2019.

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Citing papers per year: 2021: 7; 2022: 2; 2023: 2; 2024: 1; 2025: 0; 2026: 3.

15 papers.

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