RSD Difference of Gaussian
Research on RSD (Rotating Second Derivative) Difference of Gaussian (DOG) methods, and Gaussian Splatting more broadly, focuses on creating efficient and high-fidelity 3D scene representations, particularly for novel view synthesis and real-time rendering. Current efforts center on improving the accuracy and efficiency of Gaussian-based models, addressing challenges like overfitting, memory limitations, and handling diverse data types (e.g., LiDAR, raw images, event cameras). These advancements have significant implications for various applications, including 3D reconstruction, robotics, augmented/virtual reality, and computer vision tasks such as object pose estimation and image editing.
Papers
Efficient Statistics With Unknown Truncation, Polynomial Time Algorithms, Beyond Gaussians
Jane H. Lee, Anay Mehrotra, Manolis Zampetakis
GaussianBlock: Building Part-Aware Compositional and Editable 3D Scene by Primitives and Gaussians
Shuyi Jiang, Qihao Zhao, Hossein Rahmani, De Wen Soh, Jun Liu, Na Zhao
MiraGe: Editable 2D Images using Gaussian Splatting
Joanna Waczyńska, Tomasz Szczepanik, Piotr Borycki, Sławomir Tadeja, Thomas Bohné, Przemysław Spurek
Object Gaussian for Monocular 6D Pose Estimation from Sparse Views
Luqing Luo, Shichu Sun, Jiangang Yang, Linfang Zheng, Jinwei Du, Jian Liu
Data-driven 2D stationary quantum droplets and wave propagations in the amended GP equation with two potentials via deep neural networks learning
Jin Song, Zhenya Yan