Public articles linked to the same research event.
IEEE transactions on medical imaging The work proposes SurgDepth, a test-time adaptation framework that adapts natural-image-pretrained depth foundation models to surgical endoscopy without any labeled surgical data, combining two self-supervised signals (stereo photometric consistency and flip equivariance, the latter requiring only single images) with selective decoder adaptation and two tuning-free reliability mechanisms (progress-aware anchor regularization and a structural-trust safeguard for poorly-illuminated sequences); the authors report AbsRel reductions of up to 63% with stereo pairs and 42% without, improvement in four of five cross-domain evaluation settings, generalization across seven foundation models, and the lowest AbsRel (0.
The work proposes SurgDepth, a test-time adaptation framework that adapts natural-image-pretrained depth foundation models to surgical endoscopy without any labeled surgical data, combining two self-supervised signals (stereo photometric consistency and flip equivariance, the latter requiring only single images) with selective decoder adaptation and two tuning-free reliability mechanisms (progress-aware anchor regularization and a structural-trust safeguard for poorly-illuminated sequences); the authors report AbsRel reductions of up to 63% with stereo pairs and 42% without, improvement in four of five cross-domain evaluation settings, generalization across seven foundation models, and the lowest AbsRel (0.
The work proposes SurgDepth, a test-time adaptation framework that adapts natural-image-pretrained depth foundation models to surgical endoscopy without any labeled surgical data, combining two self-supervised signals (stereo photometric consistency and flip equivariance, the latter requiring only single images) with selective decoder adaptation and two tuning-free reliability mechanisms (progress-aware anchor regularization and a structural-trust safeguard for poorly-illuminated sequences); the authors report AbsRel reductions of up to 63% with stereo pairs and 42% without, improvement in four of five cross-domain evaluation settings, generalization across seven foundation models, and the lowest AbsRel (0.
The work proposes SurgDepth, a test-time adaptation framework that adapts natural-image-pretrained depth foundation models to surgical endoscopy without any labeled surgical data, combining two self-supervised signals (stereo photometric consistency and flip equivariance, the latter requiring only single images) with selective decoder adaptation and two tuning-free reliability mechanisms (progress-aware anchor regularization and a structural-trust safeguard for poorly-illuminated sequences); the authors report AbsRel reductions of up to 63% with stereo pairs and 42% without, improvement in four of five cross-domain evaluation settings, generalization across seven foundation models, and the lowest AbsRel (0.