36 citations · 93 across the 14 of their papers we have counts for
22 papers · 1 filter
On the Sins of Image Synthesis Loss for Self-supervised Depth Estimation
Zhaoshuo Li, Nathan Drenkow, Hao Ding +5
Scene depth estimation from stereo and monocular imagery is critical for extracting 3D information for downstream tasks such as scene understanding. Recently, learning-based method…
The Impact of Machine Learning on 2D/3D Registration for Image-guided Interventions: A Systematic Review and Perspective
Mathias Unberath, Cong Gao, Yicheng Hu +4
Image-based navigation is widely considered the next frontier of minimally invasive surgery. It is believed that image-based navigation will increase the access to reproducible, sa…
E-DSSR: Efficient Dynamic Surgical Scene Reconstruction with Transformer-based Stereoscopic Depth Perception
Yonghao Long, Zhaoshuo Li, Chi Hang Yee +4
Reconstructing the scene of robotic surgery from the stereo endoscopic video is an important and promising topic in surgical data science, which potentially supports many applicati…
Revisiting Stereo Depth Estimation From a Sequence-to-Sequence Perspective with Transformers
Zhaoshuo Li, Xingtong Liu, Nathan Drenkow +4
Stereo depth estimation relies on optimal correspondence matching between pixels on epipolar lines in the left and right images to infer depth. In this work, we revisit the problem…
Learning Representations of Endoscopic Videos to Detect Tool Presence Without Supervision
David Z. Li, Masaru Ishii, Russell H. Taylor +2
In this work, we explore whether it is possible to learn representations of endoscopic video frames to perform tasks such as identifying surgical tool presence without supervision.…
Extremely Dense Point Correspondences using a Learned Feature Descriptor
Xingtong Liu, Yiping Zheng, Benjamin Killeen +4
High-quality 3D reconstructions from endoscopy video play an important role in many clinical applications, including surgical navigation where they enable direct video-CT registrat…