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Related papers: WildSplatter: Feed-forward 3D Gaussian Splatting w…

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This paper presents a pose-free, feed-forward 3D Gaussian Splatting (3DGS) framework designed to handle unfavorable input views. A common rendering setup for training feed-forward approaches places a 3D object at the world origin and…

Computer Vision and Pattern Recognition · Computer Science 2025-08-07 Yuki Fujimura , Takahiro Kushida , Kazuya Kitano , Takuya Funatomi , Yasuhiro Mukaigawa

While the field of 3D scene reconstruction is dominated by NeRFs due to their photorealistic quality, 3D Gaussian Splatting (3DGS) has recently emerged, offering similar quality with real-time rendering speeds. However, both methods…

Computer Vision and Pattern Recognition · Computer Science 2024-11-01 Jonas Kulhanek , Songyou Peng , Zuzana Kukelova , Marc Pollefeys , Torsten Sattler

Sparse-view reconstruction models typically require precise camera poses, yet obtaining these parameters from sparse-view images remains challenging. We introduce FreeSplatter, a scalable feed-forward framework that generates high-quality…

Computer Vision and Pattern Recognition · Computer Science 2025-09-03 Jiale Xu , Shenghua Gao , Ying Shan

Implicit neural representation methods have shown impressive advancements in learning 3D scenes from unstructured in-the-wild photo collections but are still limited by the large computational cost of volumetric rendering. More recently, 3D…

Computer Vision and Pattern Recognition · Computer Science 2024-04-08 Hiba Dahmani , Moussab Bennehar , Nathan Piasco , Luis Roldao , Dzmitry Tsishkou

Photographs captured in unstructured tourist environments frequently exhibit variable appearances and transient occlusions, challenging accurate scene reconstruction and inducing artifacts in novel view synthesis. Although prior approaches…

Computer Vision and Pattern Recognition · Computer Science 2024-06-18 Jiacong Xu , Yiqun Mei , Vishal M. Patel

We propose a method to enhance 3D Gaussian Splatting (3DGS)~\cite{Kerbl2023}, addressing challenges in initialization, optimization, and density control. Gaussian Splatting is an alternative for rendering realistic images while supporting…

Computer Vision and Pattern Recognition · Computer Science 2025-07-02 Xingjun Wang , Lianlei Shan

3D Gaussian Splatting (3DGS) is a promising technique for 3D reconstruction, offering efficient training and rendering speeds, making it suitable for real-time applications.However, current methods require highly controlled environments (no…

Computer Vision and Pattern Recognition · Computer Science 2024-07-31 Sara Sabour , Lily Goli , George Kopanas , Mark Matthews , Dmitry Lagun , Leonidas Guibas , Alec Jacobson , David J. Fleet , Andrea Tagliasacchi

3D Gaussian Splatting has shown remarkable capabilities in novel view rendering tasks and exhibits significant potential for multi-view optimization.However, the original 3D Gaussian Splatting lacks color representation for inputs in…

Computer Vision and Pattern Recognition · Computer Science 2025-03-25 Haoran Wang , Jingwei Huang , Lu Yang , Tianchen Deng , Gaojing Zhang , Mingrui Li

3D Gaussian splatting (3DGS) has recently emerged as an alternative representation that leverages a 3D Gaussian-based representation and introduces an approximated volumetric rendering, achieving very fast rendering speed and promising…

Computer Vision and Pattern Recognition · Computer Science 2024-08-08 Joo Chan Lee , Daniel Rho , Xiangyu Sun , Jong Hwan Ko , Eunbyung Park

3D Gaussian Splatting (3DGS) has recently emerged as a fast, high-quality method for novel view synthesis (NVS). However, its use of low-degree spherical harmonics limits its ability to capture spatially varying color and view-dependent…

Computer Vision and Pattern Recognition · Computer Science 2025-11-19 Hoang Chuong Nguyen , Wei Mao , Jose M. Alvarez , Miaomiao Liu

High-quality scene reconstruction and novel view synthesis based on Gaussian Splatting (3DGS) typically require steady, high-quality photographs, often impractical to capture with handheld cameras. We present a method that adapts to camera…

Computer Vision and Pattern Recognition · Computer Science 2024-07-18 Otto Seiskari , Jerry Ylilammi , Valtteri Kaatrasalo , Pekka Rantalankila , Matias Turkulainen , Juho Kannala , Esa Rahtu , Arno Solin

Feed-forward 3D Gaussian Splatting (3DGS) enables efficient one-pass scene reconstruction, providing 3D representations for novel view synthesis without per-scene optimization. However, existing methods typically predict pixel-aligned…

Computer Vision and Pattern Recognition · Computer Science 2025-12-23 Jongmin Park , Minh-Quan Viet Bui , Juan Luis Gonzalez Bello , Jaeho Moon , Jihyong Oh , Munchurl Kim

Feed-forward 3D Gaussian Splatting (3DGS) has emerged as a highly effective solution for novel view synthesis. Existing methods predominantly rely on a \emph{pixel-aligned} Gaussian prediction paradigm, where each 2D pixel is mapped to a 3D…

Computer Vision and Pattern Recognition · Computer Science 2026-03-13 Weijie Wang , Yeqing Chen , Zeyu Zhang , Hengyu Liu , Haoxiao Wang , Zhiyuan Feng , Wenkang Qin , Feng Chen , Zheng Zhu , Donny Y. Chen , Bohan Zhuang

3D Gaussian Splatting (3DGS) has gained significant attention for its real-time, photo-realistic rendering in novel-view synthesis and 3D modeling. However, existing methods struggle with accurately modeling in-the-wild scenes affected by…

Computer Vision and Pattern Recognition · Computer Science 2025-12-05 Chuanyu Fu , Guanying Chen , Yuqi Zhang , Kunbin Yao , Yuan Xiong , Chuan Huang , Shuguang Cui , Yasuyuki Matsushita , Xiaochun Cao

3D Gaussian Splatting (3DGS) enables efficient training and fast novel view synthesis in static environments. To address challenges posed by transient objects, distractor-free 3DGS methods have emerged and shown promising results when dense…

Computer Vision and Pattern Recognition · Computer Science 2026-03-03 Yi Gu , Zhaorui Wang , Jiahang Cao , Jiaxu Wang , Mingle Zhao , Dongjun Ye , Renjing Xu

3D Gaussian Splatting (3DGS) has established itself as a leading technique for 3D reconstruction and novel view synthesis of static scenes, achieving outstanding rendering quality and fast training. However, the method does not explicitly…

Computer Vision and Pattern Recognition · Computer Science 2026-03-04 Margherita Lea Corona , Wieland Morgenstern , Peter Eisert , Anna Hilsmann

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…

Computer Vision and Pattern Recognition · Computer Science 2026-04-07 Quentin Herau , Tianshuo Xu , Depu Meng , Jiezhi Yang , Chensheng Peng , Spencer Sherk , Yihan Hu , Wei Zhan

3D Gaussian Splatting (3DGS) has shown remarkable success in synthesizing novel views given multiple views of a static scene. Yet, 3DGS faces challenges when applied to dynamic scenes because 3D Gaussian parameters need to be updated per…

Graphics · Computer Science 2024-07-08 Kai Katsumata , Duc Minh Vo , Hideki Nakayama

Recent progress in feed-forward 3D Gaussian Splatting (3DGS) has notably improved rendering quality. However, the spatially uniform and highly redundant 3DGS map generated by previous feed-forward 3DGS methods limits their integration into…

Computer Vision and Pattern Recognition · Computer Science 2026-04-06 Zicheng Zhang , Xiangting Meng , Ke Wu , Wenchao Ding

Recent advances in novel view synthesis (NVS) have enabled real-time rendering with 3D Gaussian Splatting (3DGS). However, existing methods struggle with artifacts and missing regions when rendering from viewpoints that deviate from the…

Computer Vision and Pattern Recognition · Computer Science 2025-08-11 Minsu Kim , Subin Jeon , In Cho , Mijin Yoo , Seon Joo Kim
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