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Recently, Gaussian splatting has emerged as a strong alternative to NeRF, demonstrating impressive 3D modeling capabilities while requiring only a fraction of the training and rendering time. In this paper, we show how the standard Gaussian…

Computer Vision and Pattern Recognition · Computer Science 2025-03-25 Luca Savant Aira , Gabriele Facciolo , Thibaud Ehret

3D Gaussian Splatting (3DGS) has recently advanced radiance field reconstruction by offering superior capabilities for novel view synthesis and real-time rendering speed. However, its strategy of blending optimization and adaptive density…

Computer Vision and Pattern Recognition · Computer Science 2024-11-12 Rong Liu , Rui Xu , Yue Hu , Meida Chen , Andrew Feng

The 3D Gaussian splatting methods are getting popular. However, they work directly on the signal, leading to a dense representation of the signal. Even with some techniques such as pruning or distillation, the results are still dense. In…

Computer Vision and Pattern Recognition · Computer Science 2024-05-10 Yuanhao Gong

Recovering 3D information from scenes via multi-view stereo reconstruction (MVS) and novel view synthesis (NVS) is inherently challenging, particularly in scenarios involving sparse-view setups. The advent of 3D Gaussian Splatting (3DGS)…

Computer Vision and Pattern Recognition · Computer Science 2025-05-06 Shubhendu Jena , Shishir Reddy Vutukur , Adnane Boukhayma

The advent of 3D Gaussian Splatting (3DGS) has recently brought about a revolution in the field of neural rendering, facilitating high-quality renderings at real-time speed. However, 3DGS heavily depends on the initialized point cloud…

Computer Vision and Pattern Recognition · Computer Science 2024-02-23 Kai Cheng , Xiaoxiao Long , Kaizhi Yang , Yao Yao , Wei Yin , Yuexin Ma , Wenping Wang , Xuejin Chen

3D Gaussian Splatting (3DGS) has become an emerging tool for dynamic scene reconstruction. However, existing methods focus mainly on extending static 3DGS into a time-variant representation, while overlooking the rich motion information…

Computer Vision and Pattern Recognition · Computer Science 2024-03-19 Zhiyang Guo , Wengang Zhou , Li Li , Min Wang , Houqiang Li

Recently, the 3D Gaussian splatting (3DGS) technique for real-time radiance field rendering has revolutionized the field of volumetric scene representation, providing users with an immersive experience. But in return, it also poses a large…

Computer Vision and Pattern Recognition · Computer Science 2026-03-11 Zhiye Tang , Qiudan Zhang , Lei Zhang , Junhui Hou , You Yang , Xu Wang

3D Gaussian Splatting (3DGS) achieves remarkable results in the field of surface reconstruction. However, when Gaussian normal vectors are aligned within the single-view projection plane, while the geometry appears reasonable in the current…

Computer Vision and Pattern Recognition · Computer Science 2025-08-14 Bo Jia , Yanan Guo , Ying Chang , Benkui Zhang , Ying Xie , Kangning Du , Lin Cao

The reconstruction of 3D objects from calibrated photographs represents a fundamental yet intricate challenge in the domains of computer graphics and vision. Although neural reconstruction approaches based on Neural Radiance Fields (NeRF)…

Computer Vision and Pattern Recognition · Computer Science 2025-02-21 Bang Du , Runfa Blark Li , Chen Du , Truong Nguyen

3D Gaussian Splats (3DGS) have proven a versatile rendering primitive, both for inverse rendering as well as real-time exploration of scenes. In these applications, coherence across camera frames and multiple views is crucial, be it for…

3D Gaussian Splatting (3DGS) is a new method for modeling and rendering 3D radiance fields that achieves much faster learning and rendering time compared to SOTA NeRF methods. However, it comes with a drawback in the much larger storage…

Computer Vision and Pattern Recognition · Computer Science 2024-09-30 KL Navaneet , Kossar Pourahmadi Meibodi , Soroush Abbasi Koohpayegani , Hamed Pirsiavash

Recent advances in novel view synthesis have demonstrated impressive results in fast photorealistic scene rendering through differentiable point rendering, either via Gaussian Splatting (3DGS) [Kerbl and Kopanas et al. 2023] or neural point…

Computer Vision and Pattern Recognition · Computer Science 2025-04-16 Linus Franke , Laura Fink , Marc Stamminger

Reconstructing and editing 3D objects and scenes both play crucial roles in computer graphics and computer vision. Neural radiance fields (NeRFs) can achieve realistic reconstruction and editing results but suffer from inefficiency in…

Computer Vision and Pattern Recognition · Computer Science 2024-05-24 Tong Wu , Jia-Mu Sun , Yu-Kun Lai , Yuewen Ma , Leif Kobbelt , Lin Gao

Although 3D Gaussian Splatting (3DGS) has demonstrated promising results in novel view synthesis, its performance degrades dramatically with sparse inputs and generates undesirable artifacts. As the number of training views decreases, the…

Computer Vision and Pattern Recognition · Computer Science 2025-04-15 Yexing Xu , Longguang Wang , Minglin Chen , Sheng Ao , Li Li , Yulan Guo

The advent of neural and Gaussian-based radiance field methods have achieved great success in the field of novel view synthesis. However, specular reflection remains non-trivial, as the high frequency radiance field is notoriously difficult…

Computer Vision and Pattern Recognition · Computer Science 2024-06-05 Keyang Ye , Qiming Hou , Kun Zhou

We consider the problem of novel-view synthesis (NVS) for dynamic scenes. Recent neural approaches have accomplished exceptional NVS results for static 3D scenes, but extensions to 4D time-varying scenes remain non-trivial. Prior efforts…

Computer Vision and Pattern Recognition · Computer Science 2024-07-03 Yuanxing Duan , Fangyin Wei , Qiyu Dai , Yuhang He , Wenzheng Chen , Baoquan Chen

This paper presents DENSER, an efficient and effective approach leveraging 3D Gaussian splatting (3DGS) for the reconstruction of dynamic urban environments. While several methods for photorealistic scene representations, both implicitly…

Computer Vision and Pattern Recognition · Computer Science 2024-09-17 Mahmud A. Mohamad , Gamal Elghazaly , Arthur Hubert , Raphael Frank

Underwater 3D reconstruction and appearance restoration are hindered by the complex optical properties of water, such as wavelength-dependent attenuation and scattering. Existing Neural Radiance Fields (NeRF)-based methods struggle with…

Computer Vision and Pattern Recognition · Computer Science 2026-01-28 Xinrui Zhang , Yufeng Wang , Shuangkang Fang , Zesheng Wang , Dacheng Qi , Wenrui Ding

We propose WildSplatter, a feed-forward 3D Gaussian Splatting (3DGS) model for unconstrained images with unknown camera parameters and varying lighting conditions. 3DGS is an effective scene representation that enables high-quality,…

Computer Vision and Pattern Recognition · Computer Science 2026-04-24 Yuki Fujimura , Takahiro Kushida , Kazuya Kitano , Takuya Funatomi , Yasuhiro Mukaigawa

While neural rendering has led to impressive advances in scene reconstruction and novel view synthesis, it relies heavily on accurately pre-computed camera poses. To relax this constraint, multiple efforts have been made to train Neural…

Computer Vision and Pattern Recognition · Computer Science 2024-07-31 Yang Fu , Sifei Liu , Amey Kulkarni , Jan Kautz , Alexei A. Efros , Xiaolong Wang
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