6 papers
Resolution-Flexible Decoding for Hybrid Neural Video Representations
Taiga Hayami, Masaya Takabe, Hiroshi Watanabe
Neural video representations (NVRs) represent videos using neural network parameters and, in hybrid formulations, frame-wise latent embeddings. Although hybrid NVRs can improve rec…
Accurate and Efficient Surface Reconstruction from Point Clouds via Geometry-Aware Local Adaptation
Eito Ogawa, Taiga Hayami, Hiroshi Watanabe
Point cloud surface reconstruction has improved in accuracy with advances in deep learning, enabling applications such as infrastructure inspection. Recent approaches that reconstr…
Structure-Preserving Patch Decoding for Efficient Neural Video Representation
Taiga Hayami, Kakeru Koizumi, Hiroshi Watanabe
Implicit neural representations (INRs) are the subject of extensive research, particularly in their application to modeling complex signals by mapping spatial and temporal coordina…
SR-NeRV: Improving Embedding Efficiency of Neural Video Representation via Super-Resolution
Taiga Hayami, Kakeru Koizumi, Hiroshi Watanabe
Implicit Neural Representations (INRs) have garnered significant attention for their ability to model complex signals in various domains. Recently, INR-based frameworks have shown…
Neural Video Representation for Redundancy Reduction and Consistency Preservation
Taiga Hayami, Takahiro Shindo, Shunsuke Akamatsu +1
Implicit neural representation (INR) embed various signals into neural networks. They have gained attention in recent years because of their versatility in handling diverse signal…
Implicit Neural Representation for Videos Based on Residual Connection
Taiga Hayami, Hiroshi Watanabe
Video compression technology is essential for transmitting and storing videos. Many video compression methods reduce information in videos by removing high-frequency components and…