1 citations · 1 across the 8 of their papers we have counts for
12 papers
Ask the Expert: LLM-Guided Reinforcement Learning for Autonomous Cyber Defense
Fernando Martinez, Abhishek Satyam, Tao Li +3
Policy-based reinforcement learning (RL) approaches have produced promising results for autonomous cyber defense; however, they are sample-inefficient in settings where defenders m…
SpaTime: Streaming Vision-Language Models for Spatio-temporal Reasoning
Hairong Yin, Huangying Zhan, Shin-Fang Chng +2
Embodied agents must reason about 3D space while the video is still arriving, answering questions as soon as they have observed enough of the scene. VLMs that incorporate 3D geomet…
StereoGaussians: Feed-Forward 3D Gaussian Splatting from Stereo Images
Boyuan Tian, Huangying Zhan, Zhan Li +4
Feed-forward 3D Gaussian Splatting (3DGS) enables reconstruction without per- scene optimisation, but practical stereo-camera applications require nearby-view extrapolation beyond…
4DGS-Fixer: Generative Sparse-View 4D Gaussian Splatting with Iterative Refinement Guided by Video Diffusion Priors
Haitao Huang, Shenghao Zhao, Boyuan Tian +7
This paper addresses the challenges of dynamic scene synthesis from sparse-view videos. Existing methods employ geometric priors, adaptive optimization, or density-control strategi…
FluSplat: Sparse-View 3D Editing without Test-Time Optimization
Haitao Huang, Shin-Fang Chng, Huangying Zhan +2
Recent advances in text-guided image editing and 3D Gaussian Splatting (3DGS) have enabled high-quality 3D scene manipulation. However, existing pipelines rely on iterative edit-an…
FAST3DIS: Feed-forward Anchored Scene Transformer for 3D Instance Segmentation
Changyang Li, Xueqing Huang, Shin-Fang Chng +3
While recent feed-forward 3D reconstruction models provide a strong geometric foundation for scene understanding, extending them to 3D instance segmentation typically relies on a d…
RLGS: Reinforcement Learning-Based Adaptive Hyperparameter Tuning for Gaussian Splatting
Zhan Li, Huangying Zhan, Changyang Li +2
Hyperparameter tuning in 3D Gaussian Splatting (3DGS) is a labor-intensive and expert-driven process, often resulting in inconsistent reconstructions and suboptimal results. We pro…
Understanding while Exploring: Semantics-driven Active Mapping
Liyan Chen, Huangying Zhan, Hairong Yin +2
Effective robotic autonomy in unknown environments demands proactive exploration and precise understanding of both geometry and semantics. In this paper, we propose ActiveSGM, an a…
Semantic Consistent Language Gaussian Splatting for Point-Level Open-vocabulary Querying
Hairong Yin, Huangying Zhan, Yi Xu +1
Open-vocabulary 3D scene understanding is crucial for robotics applications, such as natural language-driven manipulation, human-robot interaction, and autonomous navigation. Exist…
ActiveGAMER: Active GAussian Mapping through Efficient Rendering
Liyan Chen, Huangying Zhan, Kevin Chen +4
We introduce ActiveGAMER, an active mapping system that utilizes 3D Gaussian Splatting (3DGS) to achieve high-quality, real-time scene mapping and exploration. Unlike traditional N…
NARUTO: Neural Active Reconstruction from Uncertain Target Observations
Ziyue Feng, Huangying Zhan, Zheng Chen +6
We present NARUTO, a neural active reconstruction system that combines a hybrid neural representation with uncertainty learning, enabling high-fidelity surface reconstruction. Our…
PlanarNeRF: Online Learning of Planar Primitives with Neural Radiance Fields
Zheng Chen, Qingan Yan, Huangying Zhan +7
Identifying spatially complete planar primitives from visual data is a crucial task in computer vision. Prior methods are largely restricted to either 2D segment recovery or simpli…