Submitted by LEI YILIN LEI YILIN.
Submission data
Full name Structurally-compressed Content-preserving Iterative Optimized Network Description Our research focuses on bridging the performance-efficiency gap in stereo matching by developing SCION-MonSter, a lightweight stereo depth estimation model derived from the state-of-the-art MonSter architecture. Programming language(s) Python+CUDA Hardware RTX4090D Website https://github.com/rayring539/Monsterl-ite Source code or download URL https://github.com/rayring539/Monsterl-ite.git Submission creation date 31 Mar, 2026 Last edited 31 Mar, 2026
High-res multi-view results
Set : Test
Metric : F1 score [%]
Tolerance : 2cm
Low-res many-view results
Set : Test
Metric : F1 score [%]
Tolerance : 50cm
Low-res two-view results
Set : Test
Metric : 95% quantile [px]
Mask : non-occluded
SLAM results
Set : Test
Metric : SE(3) ATE RMSE [cm]
Max error : 8 cm
all
Method Info
No results yet.