Digitally reconstructed radiographs (DRRs) are simulated 2D X-ray images generated from 3D CT volumes, widely used in preoperative settings but limited in intraoperative applications due to computational bottlenecks, especially for accurate but heavy physics-based Monte Carlo methods. While analytical DRR renderers offer greater efficiency, they overlook anisotropic X-ray image formation phenomena, such as Compton scattering. We present a novel approach that marries realistic physics-inspired X-ray simulation with efficient, differentiable DRR generation using 3D Gaussian splatting (3DGS). Our direction-disentangled 3DGS (DDGS) method separates the radiosity contribution into isotropic and direction-dependent components, approximating complex anisotropic interactions without intricate runtime simulations. Additionally, we adapt the 3DGS initialization to account for tomography data properties, enhancing accuracy and efficiency. Our method outperforms state-of-the-art techniques in image accuracy. Furthermore, our DDGS shows promise for intraoperative applications and inverse problems such as pose registration, delivering superior registration accuracy and runtime performance compared to analytical DRR methods.
3DitScene: Editing Any Scene via Language-guided Disentangled Gaussian Splatting
The Chinese University of Hong Kong（香港中文大学）；Stanford University（斯坦福大学）；Snap Inc.（Snap公司）；University of California Los Angeles（加利福尼亚大学洛杉矶分校）；ByteDance（字节跳动）
Interpretable Single-View 3D Gaussian Splatting using Unsupervised Hierarchical Disentangled Representation Learning
Shanghai Jiao Tong University（上海交通大学）；Ningbo Institute of Digital Twin, Eastern Institute of Technology（东方理工大学宁波数字孪生研究院）；Hong Kong Polytechnic University（香港理工大学）
Disentangled 4D Gaussian Splatting: Rendering High-Resolution Dynamic World at 343 FPS
Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University（人工智能与智能学习湖北省重点实验室，中国地质大学（武汉））；School of Computer Science, Central China Normal University（计算机科学学院，中国地质大学（武汉））；National Language Resources Monitoring and Research Center for Network Media, Central China Normal University（网络媒体语言资源监测与研究中心，中国地质大学（武汉））；College of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics（人工智能学院，南京航空航天大学）；School of Data Science, Lingnan University（数据科学学院，岭南大学）
STD-GS: Exploring Frame-Event Interaction for SpatioTemporal-Disentangled Gaussian Splatting to Reconstruct High-Dynamic Scene
National Key Lab of Multispectral Information Intelligent Processing Technology（多谱信息智能处理国家级重点实验室）；School of Artificial Intelligence and Automation, Huazhong University of Science and Technology（人工智能与自动化学院，华中科技大学）；School of Computing, National University of Singapore（计算机学院，新加坡国立大学）
D$^2$GS: Depth-and-Density Guided Gaussian Splatting for Stable and Accurate Sparse-View Reconstruction
Insta360 Research（Insta360研究机构）；Tsinghua University（清华大学）；University of California, San Diego（加州大学圣地亚哥分校）；Nanyang Technological University（南洋理工大学）；Wuhan University（武汉大学）
Stereo-GS: Multi-View Stereo Vision Model for Generalizable 3D Gaussian Splatting Reconstruction
Department of Computer Science, Hong Kong Baptist University（香港 Baptist 大学计算机科学系）；NVIDIA AI Technology Center, NVIDIA（NVIDIA 人工智能技术中心）；School of Control Science and Engineering, Shandong University（控制科学与工程学院，山东大学）
R-PGA: Robust Physical Adversarial Camouflage Generation via Relightable 3D Gaussian Splatting
School of Cyber Science and Technology, Shenzhen Campus, Sun Yat-sen University（中山大学深圳校区网络空间安全学院）；School of Computing, National University of Singapore（新加坡国立大学计算机学院）；School of Intelligent Systems Engineering, Shenzhen Campus, Sun Yat-sen University（中山大学深圳校区智能工程学院）
