2 citations · 2 across the 5 of their papers we have counts for
5 papers
BayesianGS-SLAM: Uncertainty-Aware Neural Rendering SLAM via Probabilistic Formulation
Kyeongsu Kang, Seongbo Ha, Sibaek Lee +1
Neural-rendering-based SLAM relies on rendered RGB-D residuals for camera tracking and map optimization, but the reliability of these predictions can vary substantially because of…
LEGO-SLAM: Language-Embedded Gaussian Optimization SLAM
Sibaek Lee, Seongbo Ha, Kyeongsu Kang +3
Recent advances in 3D Gaussian Splatting (3DGS) have enabled Simultaneous Localization and Mapping (SLAM) systems to build photorealistic maps. However, these maps lack the open-vo…
Bayesian NeRF: Quantifying Uncertainty with Volume Density for Neural Implicit Fields
Sibeak Lee, Kyeongsu Kang, Seongbo Ha +1
We present a Bayesian Neural Radiance Field (NeRF), which explicitly quantifies uncertainty in the volume density by modeling uncertainty in the occupancy, without the need for add…
Just Flip: Flipped Observation Generation and Optimization for Neural Radiance Fields to Cover Unobserved View
Minjae Lee, Kyeongsu Kang, Hyeonwoo Yu
With the advent of Neural Radiance Field (NeRF), representing 3D scenes through multiple observations has shown remarkable improvements in performance. Since this cutting-edge tech…
Necessity Feature Correspondence Estimation for Large-scale Global Place Recognition and Relocalization
Kyeongsu Kang, Minjae Lee, Hyeonwoo Yu
Global place recognition and 3D relocalization are one of the most important components in the loop closing detection for 3D LiDAR Simultaneous Localization and Mapping (SLAM). In…