EndoVGGT uses a dynamic DeGAT graph attention module to improve depth estimation and non-rigid 3D reconstruction in surgery, reporting 24.6% PSNR and 9.1% SSIM gains on SCARED with zero-shot generalization to new domains.
In: Proceedings of the IEEE/CVF international conference on computer vision
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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Systematic tests of 27 ultrasound tasks show that unified training is more consistent than clinically-grouped training, with performance hinging on data availability and task characteristics.
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EndoVGGT: GNN-Enhanced Depth Estimation for Surgical 3D Reconstruction
EndoVGGT uses a dynamic DeGAT graph attention module to improve depth estimation and non-rigid 3D reconstruction in surgery, reporting 24.6% PSNR and 9.1% SSIM gains on SCARED with zero-shot generalization to new domains.
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Understanding Task Aggregation for Generalizable Ultrasound Foundation Models
Systematic tests of 27 ultrasound tasks show that unified training is more consistent than clinically-grouped training, with performance hinging on data availability and task characteristics.