Vision Foundation Models (VFMs) have achieved remarkable success when applied to various downstream 2D tasks. Despite their effectiveness, they often exhibit a critical lack of 3D awareness. To this end, we introduce Splat and Distill, a framework that instills robust 3D awareness into 2D VFMs by augmenting the teacher model with a fast, feed-forward 3D reconstruction pipeline. Given 2D features produced by a teacher model, our method first lifts these features into an explicit 3D Gaussian representation, in a feedforward manner. These 3D features are then ``splatted" onto novel viewpoints, producing a set of novel 2D feature maps used to supervise the student model, ``distilling" geometrically grounded knowledge. By replacing slow per-scene optimization of prior work with our feed-forward lifting approach, our framework avoids feature-averaging artifacts, creating a dynamic learning process where the teacher's consistency improves alongside that of the student. We conduct a comprehensive evaluation on a suite of downstream tasks, including monocular depth estimation, surface normal estimation, multi-view correspondence, and semantic segmentation. Our method significantly outperforms prior works, not only achieving substantial gains in 3D awareness but also enhancing the underlying semantic richness of 2D features. Project page is available at https://davidshavin4.github.io/Splat-and-Distill/
GSCompleter: A Distillation-Free Plugin for Metric-Aware 3D Gaussian Splatting Completion in Seconds
School of Computer Science and Technology, East China Normal University（东华大学计算机科学与技术学院）；Shanghai Innovation Institute（上海创新研究院）；Chongqing Key Laboratory of Precision Optics, Chongqing Institute of East China Normal University（重庆精密光学重点实验室，东华大学重庆研究院）；Shanghai Key Laboratory of Computer Software Evaluating and Testing（上海计算机软件评测测试重点实验室）；Department of Computer Science and Engineering, Shanghai Jiao Tong University（上海交通大学计算机科学与工程学院）
Parameterizing Dataset Distillation via Gaussian Splatting
The School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, China（哈尔滨工业大学深圳校区计算机科学与技术学院）；Institute of Perceptual Intelligence, Pengcheng Laboratory（感知智能研究院，鹏城实验室）
School of Computer Science and Technology, Huazhong University of Science and Technology（华中科技大学计算机科学与技术学院）；School of Software Technology, Zhejiang University（浙江大学软件学院）
GALA: Guided Attention with Language Alignment for Open Vocabulary Gaussian Splatting
Technical University of Munich（慕尼黑技术大学）；Universitat Politècnica de Catalunya（巴塞罗那技术大学）；Google（谷歌）；Munich Center for Machine Learning（慕尼黑机器学习中心）；University of Tubingen（图宾根大学）；ETH Zurich（苏黎世联邦理工学院）；Visualais
Connecting Consistency Distillation to Score Distillation for Text-to-3D Generation
Nanyang Technological University（南洋理工大学）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；The State Key Lab of Brain-Machine Intelligence, Zhejiang University（浙江大学脑机智能国家重点实验室）；School of Electrical and Electronic Engineering, Nanyang Technological University（南洋理工大学电气与电子工程学院）
SegmentDreamer: Towards High-fidelity Text-to-3D Synthesis with Segmented Consistency Trajectory Distillation
Sun Yat-sen University（中山大学）；Great Bay University（大贝大学）；Pazhou Lab (Huangpu)（琶洲实验室）；Guangdong Province Key Laboratory of Information Security Technology（广东省信息安全技术重点实验室）
Knowledge distillation (KD) is an effective model compression technique where a compact student network is taught to mimic the behavior of a complex and highly trained teacher network. In contrast, Mutual Learning (ML) provides an alternative strategy where multiple simple student networks benefit from sharing knowledge, even in the absence of a powerful but static teacher network. Motivated by these findings, we propose a single-teacher, multi-student framework that leverages both KD and ML to achieve better performance. Furthermore, an online distillation strategy is utilized to train the te…
VGD: Visual Geometry Gaussian Splatting for Feed-Forward Surround-view Driving Reconstruction
Guangdong Provincial Key Laboratory of Ultra High Definition Immersive Media Technology（广东省超高清沉浸媒体技术重点实验室）；School of Electronic and Computer Engineering, Peking University（北京大学电子与计算机工程学院）；Peng Cheng Laboratory（鹏城实验室）
