4 citations · 5 across the 6 of their papers we have counts for
6 papers
DART: Depth-as-Target Pretraining for Surgical Vision Foundation Models
John J. Han, Adam Schmidt, Muhammad Abdullah Jamal +2
Vision foundation models (VFMs) are valuable in data-scarce domains such as surgery, where a single pretrained backbone can provide rich representations for many downstream tasks.…
SCARED-C: Corrected Camera Poses for Endoscopic Depth Estimation
John J. Han, Adam Schmidt, Max Allan +2
The SCARED dataset is a widely used benchmark for endoscopic depth estimation, offering ground-truth 3D reconstructions captured with a structured light sensor. However, the depth…
On the Role of Depth in Surgical Vision Foundation Models: An Empirical Study of RGB-D Pre-training
John J. Han, Adam Schmidt, Muhammad Abdullah Jamal +4
Vision foundation models (VFMs) have emerged as powerful tools for surgical scene understanding. However, current approaches predominantly rely on unimodal RGB pre-training, overlo…
EndoPBR: Material and Lighting Estimation for Photorealistic Surgical Simulations via Physically-based Rendering
John J. Han, Jie Ying Wu
The lack of labeled datasets in 3D vision for surgical scenes inhibits the development of robust 3D reconstruction algorithms in the medical domain. Despite the popularity of Neura…
MeshBrush: Painting the Anatomical Mesh with Neural Stylization for Endoscopy
John J. Han, Ayberk Acar, Nicholas Kavoussi +1
Style transfer is a promising approach to close the sim-to-real gap in medical endoscopy. Rendering synthetic endoscopic videos by traversing pre-operative scans (such as MRI or CT…
Depth Anything in Medical Images: A Comparative Study
John J. Han, Ayberk Acar, Callahan Henry +1
Monocular depth estimation (MDE) is a critical component of many medical tracking and mapping algorithms, particularly from endoscopic or laparoscopic video. However, because groun…