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Humans can naturally identify and mentally complete occluded objects in cluttered environments. However, imparting similar cognitive ability to robotics remains challenging even with advanced reconstruction techniques, which models scenes…

Computer Vision and Pattern Recognition · Computer Science 2025-07-22 Zesong Yang , Bangbang Yang , Wenqi Dong , Chenxuan Cao , Liyuan Cui , Yuewen Ma , Zhaopeng Cui , Hujun Bao

Rendering novel view images in dynamic scenes is a crucial yet challenging task. Current methods mainly utilize NeRF-based methods to represent the static scene and an additional time-variant MLP to model scene deformations, resulting in…

Computer Vision and Pattern Recognition · Computer Science 2024-06-07 Diwen Wan , Ruijie Lu , Gang Zeng

3D Gaussian Splatting (3DGS) has emerged as a powerful representation for neural scene reconstruction, offering high-quality novel view synthesis while maintaining computational efficiency. In this paper, we extend the capabilities of 3DGS…

Computer Vision and Pattern Recognition · Computer Science 2025-06-10 Jens Piekenbrinck , Christian Schmidt , Alexander Hermans , Narunas Vaskevicius , Timm Linder , Bastian Leibe

Gaussian Splatting has emerged as a leading method for novel view synthesis, offering superior training efficiency and real-time inference compared to NeRF approaches, while still delivering high-quality reconstructions. Beyond view…

Computer Vision and Pattern Recognition · Computer Science 2025-11-25 Lorenzo Rutayisire , Nicola Capodieci , Fabio Pellacini

3D Gaussian Splatting (3DGS) is a powerful reconstruction technique, but it needs to be initialized from accurate camera poses and high-fidelity point clouds. Typically, the initialization is taken from Structure-from-Motion (SfM)…

Computer Vision and Pattern Recognition · Computer Science 2025-04-15 Jizong Peng , Tze Ho Elden Tse , Kai Xu , Wenchao Gao , Angela Yao

The recent 3D Gaussian splatting (3D-GS) has shown remarkable rendering fidelity and efficiency compared to NeRF-based neural scene representations. While demonstrating the potential for real-time rendering, 3D-GS encounters rendering…

Computer Vision and Pattern Recognition · Computer Science 2024-10-18 Kerui Ren , Lihan Jiang , Tao Lu , Mulin Yu , Linning Xu , Zhangkai Ni , Bo Dai

Transient objects in casual multi-view captures cause ghosting artifacts in 3D Gaussian Splatting (3DGS) reconstruction. Existing solutions relied on scene decomposition at significant memory cost or on motion-based heuristics that were…

Computer Vision and Pattern Recognition · Computer Science 2026-02-18 Aditi Prabakaran , Priyesh Shukla

We propose a 3D novel sparse-view synthesis framework for unconstrained real-world scenarios that contain distractors. Unlike existing methods that primarily perform novel-view synthesis from a sparse set of constrained images without…

Computer Vision and Pattern Recognition · Computer Science 2026-05-01 Wongi Park , Jordan A. James , Myeongseok Nam , Minjae Lee , Soomok Lee , Sang-Hyun Lee , William J. Beksi

Recent advancements in radiance fields have opened new avenues for creating high-quality 3D assets and scenes. Style transfer can enhance these 3D assets with diverse artistic styles, transforming creative expression. However, existing…

Computer Vision and Pattern Recognition · Computer Science 2024-07-15 Sahil Jain , Avik Kuthiala , Prabhdeep Singh Sethi , Prakanshul Saxena

In recent years, Neural Radiance Fields (NeRF) has revolutionized three-dimensional (3D) reconstruction with its implicit representation. Building upon NeRF, 3D Gaussian Splatting (3D-GS) has departed from the implicit representation of…

Computer Vision and Pattern Recognition · Computer Science 2024-05-29 Bin Zhang , Bi Zeng , Zexin Peng

Efficient scene representations are essential for many real-world applications, especially those involving spatial measurement. Although current NeRF-based methods have achieved impressive results in reconstructing building-scale scenes,…

Computer Vision and Pattern Recognition · Computer Science 2025-05-19 Jianlin Guo , Haihong Xiao , Wenxiong Kang

3D Gaussian Splatting has recently emerged as a highly promising technique for modeling of static 3D scenes. In contrast to Neural Radiance Fields, it utilizes efficient rasterization allowing for very fast rendering at high-quality.…

Computer Vision and Pattern Recognition · Computer Science 2024-12-02 Wieland Morgenstern , Florian Barthel , Anna Hilsmann , Peter Eisert

3D Gaussian Splatting (3D-GS) achieves real-time photorealistic novel view synthesis, yet struggles with complex scenes due to over-reconstruction artifacts, manifesting as local blurring and needle-shape distortions. While recent…

Computer Vision and Pattern Recognition · Computer Science 2025-10-23 Yuxin Cheng , Binxiao Huang , Wenyong Zhou , Taiqiang Wu , Zhengwu Liu , Graziano Chesi , Ngai Wong

We introduce a novel framework for reconstructing dynamic human-object interactions from monocular video that overcomes challenges associated with occlusions and temporal inconsistencies. Traditional 3D reconstruction methods typically…

Computer Vision and Pattern Recognition · Computer Science 2025-09-16 Hyungjun Doh , Dong In Lee , Seunggeun Chi , Pin-Hao Huang , Kwonjoon Lee , Sangpil Kim , Karthik Ramani

This paper proposes a novel framework for large-scale scene reconstruction based on 3D Gaussian splatting (3DGS) and aims to address the scalability and accuracy challenges faced by existing methods. For tackling the scalability issue, we…

Computer Vision and Pattern Recognition · Computer Science 2024-09-25 Hanyue Zhang , Zhiliu Yang , Xinhe Zuo , Yuxin Tong , Ying Long , Chen Liu

Novel view synthesis has shown rapid progress recently, with methods capable of producing increasingly photorealistic results. 3D Gaussian Splatting has emerged as a promising method, producing high-quality renderings of scenes and enabling…

Computer Vision and Pattern Recognition · Computer Science 2024-07-19 Richard Shaw , Michal Nazarczuk , Jifei Song , Arthur Moreau , Sibi Catley-Chandar , Helisa Dhamo , Eduardo Perez-Pellitero

We propose two novel ideas (adoption of deferred rendering and mesh-based representation) to improve the quality of 3D Gaussian splatting (3DGS) based inverse rendering. We first report a problem incurred by hidden Gaussians, where…

Graphics · Computer Science 2024-09-17 Euntae Choi , Sungjoo Yoo

Generating high-quality 3D content requires models capable of learning robust distributions of complex scenes and the real-world objects within them. Recent Gaussian-based 3D reconstruction techniques have achieved impressive results in…

Image and Video Processing · Electrical Eng. & Systems 2024-12-16 Kevin Miao , Harsh Agrawal , Qihang Zhang , Federico Semeraro , Marco Cavallo , Jiatao Gu , Alexander Toshev

Gaussian Splatting (GS) enables immersive rendering, but realistic 3D object-scene composition remains challenging. Baked appearance and shadow information in GS radiance fields cause inconsistencies when combining objects and scenes.…

Computer Vision and Pattern Recognition · Computer Science 2025-10-10 Jian Gao , Mengqi Yuan , Yifei Zeng , Chang Zeng , Zhihao Li , Zhenyu Chen , Weichao Qiu , Xiao-Xiao Long , Hao Zhu , Xun Cao , Yao Yao

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
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