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
Splatter-360: Generalizable 360$^{\circ}$ Gaussian Splatting for Wide-baseline Panoramic Images
Zheng Chen, Chenming Wu, Zhelun Shen, Chen Zhao, Weicai Ye, Haocheng Feng, Errui Ding, Song-Hai Zhang
Generative Densification: Learning to Densify Gaussians for High-Fidelity Generalizable 3D Reconstruction
Seungtae Nam, Xiangyu Sun, Gyeongjin Kang, Younggeun Lee, Seungjun Oh, Eunbyung Park
Obstacle-aware Gaussian Process Regression
Gaurav Shrivastava