Zobrazeno 1 - 10
of 98
pro vyhledávání: '"Soundararajan, Rajiv"'
With the rise of marine exploration, underwater imaging has gained significant attention as a research topic. Underwater video enhancement has become crucial for real-time computer vision tasks in marine exploration. However, most existing methods fo
Externí odkaz:
http://arxiv.org/abs/2411.05886
The design of no-reference (NR) image quality assessment (IQA) algorithms is extremely important to benchmark and calibrate user experiences in modern visual systems. A major drawback of state-of-the-art NR-IQA methods is their limited ability to gen
Externí odkaz:
http://arxiv.org/abs/2406.04654
Neural Radiance Fields (NeRF) show impressive performance in photo-realistic free-view rendering of scenes. Recent improvements on the NeRF such as TensoRF and ZipNeRF employ explicit models for faster optimization and rendering, as compared to the N
Externí odkaz:
http://arxiv.org/abs/2404.19015
Designing a 3D representation of a dynamic scene for fast optimization and rendering is a challenging task. While recent explicit representations enable fast learning and rendering of dynamic radiance fields, they require a dense set of input viewpoi
Externí odkaz:
http://arxiv.org/abs/2404.11669
Perceptual quality assessment of user generated content (UGC) videos is challenging due to the requirement of large scale human annotated videos for training. In this work, we address this challenge by first designing a self-supervised Spatio-Tempora
Externí odkaz:
http://arxiv.org/abs/2312.15425
No-reference (NR) image quality assessment (IQA) is an important tool in enhancing the user experience in diverse visual applications. A major drawback of state-of-the-art NR-IQA techniques is their reliance on a large number of human annotations to
Externí odkaz:
http://arxiv.org/abs/2312.04838
Neural Radiance Fields (NeRF) show impressive performance for the photorealistic free-view rendering of scenes. However, NeRFs require dense sampling of images in the given scene, and their performance degrades significantly when only a sparse set of
Externí odkaz:
http://arxiv.org/abs/2309.03955
While the design of blind image quality assessment (IQA) algorithms has improved significantly, the distribution shift between the training and testing scenarios often leads to a poor performance of these methods at inference time. This motivates the
Externí odkaz:
http://arxiv.org/abs/2307.14735
Publikováno v:
ACM SIGGRAPH 2023 Conference Proceedings, Article 71, Pages 1-11
Neural radiance fields (NeRF) have achieved impressive performances in view synthesis by encoding neural representations of a scene. However, NeRFs require hundreds of images per scene to synthesize photo-realistic novel views. Training them on spars
Externí odkaz:
http://arxiv.org/abs/2305.00041
Designing learning-based no-reference (NR) video quality assessment (VQA) algorithms for camera-captured videos is cumbersome due to the requirement of a large number of human annotations of quality. In this work, we propose a semi-supervised learnin
Externí odkaz:
http://arxiv.org/abs/2211.17075