Tootfinder

Dimension-Decomposed Learning for Quadrotor Geometric Attitude Control with Almost Global Exponential Convergence on SO(3)
Tianhua Gao, Masashi Izumita, Kohji Tomita, Akiya Kamimura
https://arxiv.org/abs/2508.14422

Dimension-Decomposed Learning for Quadrotor Geometric Attitude Control with Almost Global Exponential Convergence on SO(3)
This paper introduces a lightweight and interpretable online learning approach called Dimension-Decomposed Learning (DiD-L) for disturbance identification in quadrotor geometric attitude control. As a module instance of DiD-L, we propose the Sliced Adaptive-Neuro Mapping (SANM). Specifically, to address underlying underfitting problems, the high-dimensional mapping for online identification is axially ``sliced" into multiple low-dimensional submappings (slices). In this way, the complex high-di…

D$^2$GS: Depth-and-Density Guided Gaussian Splatting for Stable and Accurate Sparse-View Reconstruction
Meixi Song, Xin Lin, Dizhe Zhang, Haodong Li, Xiangtai Li, Bo Du, Lu Qi
https://arxiv.org/abs/2510.08566

D$^2$GS: Depth-and-Density Guided Gaussian Splatting for Stable and Accurate Sparse-View Reconstruction
Recent advances in 3D Gaussian Splatting (3DGS) enable real-time, high-fidelity novel view synthesis (NVS) with explicit 3D representations. However, performance degradation and instability remain significant under sparse-view conditions. In this work, we identify two key failure modes under sparse-view conditions: overfitting in regions with excessive Gaussian density near the camera, and underfitting in distant areas with insufficient Gaussian coverage. To address these challenges, we propose…