2 citations · 3 across the 5 of their papers we have counts for
5 papers
THUD++: Large-Scale Dynamic Indoor Scene Dataset and Benchmark for Mobile Robots
Zeshun Li, Fuhao Li, Wanting Zhang +4
Most existing mobile robotic datasets primarily capture static scenes, limiting their utility for evaluating robotic performance in dynamic environments. To address this, we presen…
2DGS-Room: Seed-Guided 2D Gaussian Splatting with Geometric Constrains for High-Fidelity Indoor Scene Reconstruction
Wanting Zhang, Haodong Xiang, Zhichao Liao +3
The reconstruction of indoor scenes remains challenging due to the inherent complexity of spatial structures and the prevalence of textureless regions. Recent advancements in 3D Ga…
Fine-detailed Neural Indoor Scene Reconstruction using multi-level importance sampling and multi-view consistency
Xinghui Li, Yuchen Ji, Xiansong Lai +1
Recently, neural implicit 3D reconstruction in indoor scenarios has become popular due to its simplicity and impressive performance. Previous works could produce complete results l…
Mobile Robot Oriented Large-Scale Indoor Dataset for Dynamic Scene Understanding
Yifan Tang, Cong Tai, Fangxing Chen +5
Most existing robotic datasets capture static scene data and thus are limited in evaluating robots' dynamic performance. To address this, we present a mobile robot oriented large-s…
GaussianRoom: Improving 3D Gaussian Splatting with SDF Guidance and Monocular Cues for Indoor Scene Reconstruction
Haodong Xiang, Xinghui Li, Kai Cheng +5
Embodied intelligence requires precise reconstruction and rendering to simulate large-scale real-world data. Although 3D Gaussian Splatting (3DGS) has recently demonstrated high-qu…