Feed-forward 3D Gaussian Splatting methods have achieved impressive reconstruction quality for autonomous driving scenes, yet they entangle scene geometry with transient appearance properties such as lighting, weather, and time of day. This coupling prevents relighting, appearance transfer, and consistent rendering across multi-traversal data captured under varying environmental conditions. We present SpectralSplat, a method that disentangles appearance from geometry within a feed-forward Gaussian Splatting framework. Our key insight is to factor color prediction into an appearance-agnostic base stream and and appearance-conditioned adapted stream, both produced by a shared MLP conditioned on a global appearance embedding derived from DINOv2 features. To enforce disentanglement, we train with paired observations generated by a hybrid relighting pipeline that combines physics-based intrinsic decomposition with diffusion based generative refinement, and supervise with complementary consistency, reconstruction, cross-appearance, and base color losses. We further introduce an appearance-adaptable temporal history that stores appearance-agnostic features, enabling accumulated Gaussians to be re-rendered under arbitrary target appearances. Experiments demonstrate that SpectralSplat preserves the reconstruction quality of the underlying backbone while enabling controllable appearance transfer and temporally consistent relighting across driving sequences.
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）
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）
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）
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）
Scaling Up Occupancy-centric Driving Scene Generation: Dataset and Method
Shanghai Jiao Tong University（上海交通大学）；Eastern Institute of Technology（东部技术研究院）；School of Electronic Information and Electrical Engineering（电子信息与电气工程学院）；Li Auto（力汽车）；National University of Singapore（新加坡国立大学）；Tsinghua University（清华大学）；Ningbo Key Laboratory of Spatial Intelligence and Digital Derivative（宁波空间智能与数字衍生实验室）；Ningbo Institute of Digital Twin（宁波数字孪生研究院）
