2 citations · 2 across the 7 of their papers we have counts for
8 papers
Training-Free Target-Aware Refinement of 3D Gaussian Language Map
Sibaek Lee, Kyeongsu Kang, Seongbo Ha +9
Distilling vision-language features into 3D Gaussian Splatting yields a language map that can be queried with arbitrary text. In deployment, however, a small set of user-specified…
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…
LAMP: Implicit Language Map for Robot Navigation
Sibaek Lee, Hyeonwoo Yu, Giseop Kim +1
Recent advances in vision-language models have made zero-shot navigation feasible, enabling robots to follow natural language instructions without requiring labeling. However, exis…
LangGS-SLAM: Real-Time Language-Feature Gaussian Splatting SLAM
Seongbo Ha, Sibaek Lee, Kyungsu Kang +3
In this paper, we propose a RGB-D SLAM system that reconstructs a language-aligned dense feature field while sustaining low-latency tracking and mapping. First, we introduce a Top-…
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…
TriLift: Interpolation-Free Tri-Plane Lifting for Efficient 3D Perception on Embedded Systems
Sibaek Lee, Jiung Yeon, Hyeonwoo Yu
Dense 3D convolutions provide high accuracy for perception but are too computationally expensive for real-time robotic systems. Existing tri-plane methods rely on 2D image features…
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…