Advancing Low-Rank and Local Low-Rank Matrix Approximation in Medical Imaging: A Systematic Literature Review and Future Directions
The large volume and complexity of medical imaging datasets are bottlenecks for storage,
transmission, and processing. To tackle these challenges, the application of low-rank matrix …
transmission, and processing. To tackle these challenges, the application of low-rank matrix …
[HTML][HTML] Interior structural change detection using a 3D model and LiDAR segmentation
Detecting changes of indoor environments with respect to a 3D model is important for
building monitoring and management. Existing change detection methods based on LiDAR …
building monitoring and management. Existing change detection methods based on LiDAR …
Empointmovseg: sparse tensor-based moving-object segmentation in 3-d lidar point clouds for autonomous driving-embedded system
Object segmentation is a per-pixel label prediction task that targets at providing context
analysis for autonomous driving. Moving-object segmentation (MOS) serves as a subbranch …
analysis for autonomous driving. Moving-object segmentation (MOS) serves as a subbranch …
OccSora: 4D Occupancy Generation Models as World Simulators for Autonomous Driving
Understanding the evolution of 3D scenes is important for effective autonomous driving.
While conventional methods mode scene development with the motion of individual …
While conventional methods mode scene development with the motion of individual …
Structural Reparameterization Network on Point Cloud Semantic Segmentation
ZJ Li, K Jia, YX Zhao, WW Huang - International Conference on Image and …, 2023 - Springer
In recent years, 3D point cloud semantic segmentation has made remarkable progress.
However, most existing work focuses on designing intricate structures to aggregate local …
However, most existing work focuses on designing intricate structures to aggregate local …
Tail-Net: Extracting Lowest Singular Triplets for Big Data Applications
SVD serves as an exploratory tool in identifying the dominant features in the form of top rank-
r singular factors corresponding to the largest singular values. For Big Data applications it is …
r singular factors corresponding to the largest singular values. For Big Data applications it is …