Search Results for author:
Found 8 papers, 6 papers with code
When Less Is More: A Sparse Facial Motion Structure For Listening Motion Learning
This study proposes a novel method for representing and predicting non-verbal facial motion by encoding long sequences into a sparse sequence of keyframes and transition frames.
A-SCoRe: Attention-based Scene Coordinate Regression for wide-ranging scenarios
While state-of-the art methods that relies on feature matching have proven to be accurate for visual localization, its requirements for storage and compute are burdens.
Improved 3D Point-Line Mapping Regression for Camera Relocalization
To this end, we introduce a new architecture that learns to prioritize each feature independently before combining them for localization.
PainDiffusion: Can robot express pain?
Pain is a more intuitive and user-friendly way of communicating problems, making it especially useful in rehabilitation nurse training robots.
Leveraging Neural Radiance Field in Descriptor Synthesis for Keypoints Scene Coordinate Regression
Classical structural-based visual localization methods offer high accuracy but face trade-offs in terms of storage, speed, and privacy.
Representing 3D sparse map points and lines for camera relocalization
Recent advancements in visual localization and mapping have demonstrated considerable success in integrating point and line features.
D2S: Representing sparse descriptors and 3D coordinates for camera relocalization
In this study, we propose a direct learning-based approach that utilizes a simple network named D2S to represent complex local descriptors and their scene coordinates.
Fast and Lightweight Scene Regressor for Camera Relocalization
The proposed approach uses sparse descriptors to regress the scene coordinates, instead of a dense RGB image.
