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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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Rotationally-Consistent Novel View Synthesis for Humans
Proceedings of the 28th ACM International Conference on Multimedia, 2020Human novel view synthesis aims to synthesize target views of a human subject given input images taken from one or more reference viewpoints. Despite significant advances in model-free novel view synthesis, existing methods present two major limitations when applied to complex shapes like humans.
Youngjoong Kwon +5 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
semanticscholar +1 more source
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
semanticscholar +1 more source
Bi-layer inpainting for novel view synthesis
2011 18th IEEE International Conference on Image Processing, 2011We present a bi-layer inpainting method for synthesizing novel views from a single color image and its corresponding depth map under the exemplar-based inpainting framework. Unlike conventional image inpainting, the decisions of the inpainting direction and the sample regions are important to inpaint disoccluded regions which disclose hidden background
Hwasup Lim +5 more
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Wild-GS: Real-Time Novel View Synthesis from Unconstrained Photo Collections
Neural Information Processing SystemsPhotographs captured in unstructured tourist environments frequently exhibit variable appearances and transient occlusions, challenging accurate scene reconstruction and inducing artifacts in novel view synthesis.
Jiacong Xu, Yiqun Mei, Vishal M. Patel
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Novel view synthesis using a translating camera
Pattern Recognition Letters, 2005This paper addresses the problem of synthesizing novel views of a scene using images taken by an uncalibrated translating camera. We propose a method for synthesis of views corresponding to translational motion of the camera. Our scheme can handle occlusions and changes in visibility in the synthesized views.
Geetika Sharma +4 more
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True Self-Supervised Novel View Synthesis is Transferable
arXiv.orgIn this paper, we identify that the key criterion for determining whether a model is truly capable of novel view synthesis (NVS) is transferability: Whether any pose representation extracted from one video sequence can be used to re-render the same ...
Thomas W. Mitchel +2 more
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HDR-GS: Efficient High Dynamic Range Novel View Synthesis at 1000x Speed via Gaussian Splatting
Neural Information Processing SystemsHigh dynamic range (HDR) novel view synthesis (NVS) aims to create photorealistic images from novel viewpoints using HDR imaging techniques. The rendered HDR images capture a wider range of brightness levels containing more details of the scene than ...
Yuanhao Cai +7 more
semanticscholar +1 more source
High-Fidelity Novel View Synthesis via Splatting-Guided Diffusion
International Conference on Computer Graphics and Interactive TechniquesDespite recent advances in Novel View Synthesis (NVS), generating high-fidelity views from single or sparse observations remains challenging. Existing splatting-based approaches often produce distorted geometry due to splatting errors.
Xiang Zhang +4 more
semanticscholar +1 more source

