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Novel View Synthesis With Skip Connections
2020 IEEE International Conference on Image Processing (ICIP), 2020Novel view synthesis is the task of synthesizing an image of an object at an arbitrary viewpoint given one or a few views of the object [1]. The output image of novel view synthesis exhibits a significant structural change from the input. Because of the large change, the skip connections or U-Net architecture, which can sustain the multi-level ...
Juhyeon Kim, Young Min Kim
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Forward Flow for Novel View Synthesis of Dynamic Scenes
IEEE International Conference on Computer Vision, 2023This paper proposes a neural radiance field (NeRF) approach for novel view synthesis of dynamic scenes using forward warping. Existing methods often adopt a static NeRF to represent the canonical space, and render dynamic images at other time steps by ...
Xiang Guo +8 more
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ViewCrafter: Taming Video Diffusion Models for High-fidelity Novel View Synthesis
IEEE Transactions on Pattern Analysis and Machine IntelligenceDespite recent advancements in neural 3D reconstruction, the dependence on dense multi-view captures restricts their broader applicability. In this work, we propose ViewCrafter, a novel method for synthesizing high-fidelity novel views from single or ...
Wangbo Yu +9 more
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Generative Camera Dolly: Extreme Monocular Dynamic Novel View Synthesis
European Conference on Computer VisionAccurate reconstruction of complex dynamic scenes from just a single viewpoint continues to be a challenging task in computer vision. Current dynamic novel view synthesis methods typically require videos from many different camera viewpoints ...
Basile Van Hoorick +8 more
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On-the-fly Reconstruction for Large-Scale Novel View Synthesis from Unposed Images
ACM Transactions on GraphicsRadiance field methods such as 3D Gaussian Splatting (3DGS) allow easy reconstruction from photos, enabling free-viewpoint navigation. Nonetheless, pose estimation using Structure from Motion and 3DGS optimization can still each take between minutes and ...
Andreas Meuleman +4 more
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Cross-Ray Neural Radiance Fields for Novel-view Synthesis from Unconstrained Image Collections
IEEE International Conference on Computer Vision, 2023Neural Radiance Fields (NeRF) is a revolutionary approach for rendering scenes by sampling a single ray per pixel and it has demonstrated impressive capabilities in novel-view synthesis from static scene images.
Yifan Yang +4 more
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Radiative Gaussian Splatting for Efficient X-ray Novel View Synthesis
European Conference on Computer VisionX-ray is widely applied for transmission imaging due to its stronger penetration than natural light. When rendering novel view X-ray projections, existing methods mainly based on NeRF suffer from long training time and slow inference speed. In this paper,
Yuanhao Cai +7 more
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Novel view synthesis in tensor space
Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2002We present a new method for synthesizing novel views of a 3D scene from few model images in full correspondence. The core of this work is the derivation of a tensorial operator that describes the transformation from a given tensor of three views to a novel tensor of a new configuration of three views.
S. Avidan, A. Shashua
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CoMapGS: Covisibility Map-Based Gaussian Splatting for Sparse Novel View Synthesis
Computer Vision and Pattern RecognitionWe propose Covisibility Map-Based Gaussian Splatting (CoMapGS), designed to recover underrepresented sparse regions in sparse novel view synthesis. CoMapGS addresses both high- and low-uncertainty regions by constructing covisibility maps, enhancing ...
Youngkyoon Jang +1 more
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View Independent Generative Adversarial Network for Novel View Synthesis
2019 IEEE/CVF International Conference on Computer Vision (ICCV), 2019Synthesizing novel views from a 2D image requires to infer 3D structure and project it back to 2D from a new viewpoint. In this paper, we propose an encoder-decoder based generative adversarial network VI-GAN to tackle this problem. Our method is to let the network, after seeing many images of objects belonging to the same category in different views ...
Xiaogang Xu, Yingcong Chen, Jiaya Jia
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