6 citations · 6 across the 2 of their papers we have counts for
6 papers · 1 filter
DenseSplat: Densifying Gaussian Splatting SLAM with Neural Radiance Prior
Mingrui Li, Shuhong Liu, Tianchen Deng +1
Gaussian SLAM systems excel in real-time rendering and fine-grained reconstruction compared to NeRF-based systems. However, their reliance on extensive keyframes is impractical for…
STG-Avatar: Animatable Human Avatars via Spacetime Gaussian
Guangan Jiang, Tianzi Zhang, Dong Li +4
Realistic animatable human avatars from monocular videos are crucial for advancing human-robot interaction and enhancing immersive virtual experiences. While recent research on 3DG…
Dy3DGS-SLAM: Monocular 3D Gaussian Splatting SLAM for Dynamic Environments
Mingrui Li, Yiming Zhou, Hongxing Zhou +4
Current Simultaneous Localization and Mapping (SLAM) methods based on Neural Radiance Fields (NeRF) or 3D Gaussian Splatting excel in reconstructing static 3D scenes but struggle w…
SGS-SLAM: Semantic Gaussian Splatting For Neural Dense SLAM
Mingrui Li, Shuhong Liu, Heng Zhou +4
We present SGS-SLAM, the first semantic visual SLAM system based on Gaussian Splatting. It incorporates appearance, geometry, and semantic features through multi-channel optimizati…
DDN-SLAM: Real-time Dense Dynamic Neural Implicit SLAM
Mingrui Li, Yiming Zhou, Guangan Jiang +3
SLAM systems based on NeRF have demonstrated superior performance in rendering quality and scene reconstruction for static environments compared to traditional dense SLAM. However,…
MoD-SLAM: Monocular Dense Mapping for Unbounded 3D Scene Reconstruction
Heng Zhou, Zhetao Guo, Shuhong Liu +4
Monocular SLAM has received a lot of attention due to its simple RGB inputs and the lifting of complex sensor constraints. However, existing monocular SLAM systems are designed for…