Recently, generalizable feed-forward methods based on 3D Gaussian Splatting have gained significant attention for their potential to reconstruct 3D scenes using finite resources. These approaches create a 3D radiance field, parameterized by per-pixel 3D Gaussian primitives, from just a few images in a single forward pass. However, unlike multi-view methods that benefit from cross-view correspondences, 3D scene reconstruction with a single-view image remains an underexplored area. In this work, we introduce CATSplat, a novel generalizable transformer-based framework designed to break through the inherent constraints in monocular settings. First, we propose leveraging textual guidance from a visual-language model to complement insufficient information from a single image. By incorporating scene-specific contextual details from text embeddings through cross-attention, we pave the way for context-aware 3D scene reconstruction beyond relying solely on visual cues. Moreover, we advocate utilizing spatial guidance from 3D point features toward comprehensive geometric understanding under single-view settings. With 3D priors, image features can capture rich structural insights for predicting 3D Gaussians without multi-view techniques. Extensive experiments on large-scale datasets demonstrate the state-of-the-art performance of CATSplat in single-view 3D scene reconstruction with high-quality novel view synthesis.
Instant GaussianImage: A Generalizable and Self-Adaptive Image Representation via 2D Gaussian Splatting
School of Computer Science & Technology, Huazhong University of Science and Technology（华中科技大学计算机科学与技术学院）；NERCGIS, China University of Geoscience (Wuhan)（中国地质大学（武汉）NERCGIS）；Department of Mathematics, University of South Carolina（南卡罗来纳大学数学系）
REArtGS++: Generalizable Articulation Reconstruction with Temporal Geometry Constraint via Planar Gaussian Splatting
Hefei Institutes of Physical Science, Chinese Academy of Sciences（合肥物理科学研究院，中国科学院）；University of Science and Technology of China（中国科学技术大学）；Hefei University of Technology（合肥工业大学）；RoboticsX, Tencent（腾讯机器人院）；Shanghai Jiao Tong University（上海交通大学）
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）
MVSGaussian: Fast Generalizable Gaussian Splatting Reconstruction from Multi-View Stereo
Great Bay University（湾谷大学）；Huazhong University of Science and Technology（华中科技大学）；Nanyang Technological University（南洋理工大学）；Shanghai AI Laboratory（上海人工智能实验室）
