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
MVG-Splatting: Multi-View Guided Gaussian Splatting with Adaptive Quantile-Based Geometric Consistency Densification
Zhuoxiao Li, Shanliang Yao, Yijie Chu, Angel F. Garcia-Fernandez, Yong Yue, Eng Gee Lim, Xiaohui Zhu
Ev-GS: Event-based Gaussian splatting for Efficient and Accurate Radiance Field Rendering
Jingqian Wu, Shuo Zhu, Chutian Wang, Edmund Y. Lam
DC-Gaussian: Improving 3D Gaussian Splatting for Reflective Dash Cam Videos
Linhan Wang, Kai Cheng, Shuo Lei, Shengkun Wang, Wei Yin, Chenyang Lei, Xiaoxiao Long, Chang-Tien Lu
GaussianFormer: Scene as Gaussians for Vision-Based 3D Semantic Occupancy Prediction
Yuanhui Huang, Wenzhao Zheng, Yunpeng Zhang, Jie Zhou, Jiwen Lu