11 citations · 16 across the 7 of their papers we have counts for
11 papers
Neuralangelo: High-Fidelity Neural Surface Reconstruction
Zhaoshuo Li, Thomas Müller, Alex Evans +4
Neural surface reconstruction has been shown to be powerful for recovering dense 3D surfaces via image-based neural rendering. However, current methods struggle to recover detailed…
Rethinking Causality-driven Robot Tool Segmentation with Temporal Constraints
Hao Ding, Jie Ying Wu, Zhaoshuo Li +1
Purpose: Vision-based robot tool segmentation plays a fundamental role in surgical robots and downstream tasks. CaRTS, based on a complementary causal model, has shown promising pe…
Context-Enhanced Stereo Transformer
Weiyu Guo, Zhaoshuo Li, Yongkui Yang +5
Stereo depth estimation is of great interest for computer vision research. However, existing methods struggles to generalize and predict reliably in hazardous regions, such as larg…
SAGE: SLAM with Appearance and Geometry Prior for Endoscopy
Xingtong Liu, Zhaoshuo Li, Masaru Ishii +3
In endoscopy, many applications (e.g., surgical navigation) would benefit from a real-time method that can simultaneously track the endoscope and reconstruct the dense 3D geometry…
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…
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…