Search Results for author: Keisuke Nonaka

Found 7 papers, 0 papers with code

Fast graph-based denoising for point cloud color information

no code implementations18 Jan 2024 Ryosuke Watanabe, Keisuke Nonaka, Eduardo Pavez, Tatsuya Kobayashi, Antonio Ortega

Second, we propose a fast noise level estimation method using eigenvalues of the covariance matrix on a graph.

Denoising graph construction

SCP: Spherical-Coordinate-based Learned Point Cloud Compression

no code implementations24 Aug 2023 Ao Luo, Linxin Song, Keisuke Nonaka, Kyohei Unno, Heming Sun, Masayuki Goto, Jiro Katto

In recent years, the task of learned point cloud compression has gained prominence.

Motion estimation and filtered prediction for dynamic point cloud attribute compression

no code implementations15 Oct 2022 Haoran Hong, Eduardo Pavez, Antonio Ortega, Ryosuke Watanabe, Keisuke Nonaka

The scheme includes integer-precision motion estimation and an adaptive graph based in-loop filtering scheme for improved attribute prediction.

Attribute Motion Estimation

Fractional Motion Estimation for Point Cloud Compression

no code implementations1 Feb 2022 Haoran Hong, Eduardo Pavez, Antonio Ortega, Ryosuke Watanabe, Keisuke Nonaka

Motivated by the success of fractional pixel motion in video coding, we explore the design of motion estimation with fractional-voxel resolution for compression of color attributes of dynamic 3D point clouds.

Motion Compensation Motion Estimation

A Fast Free-viewpoint Video Synthesis Algorithm for Sports Scenes

no code implementations28 Mar 2019 Jun Chen, Ryosuke Watanabe, Keisuke Nonaka, Tomoaki Konno, Hiroshi Sankoh, Sei Naito

In this paper, we report on a parallel freeviewpoint video synthesis algorithm that can efficiently reconstruct a high-quality 3D scene representation of sports scenes.

Efficient Parallel Connected Components Labeling with a Coarse-to-fine Strategy

no code implementations28 Dec 2017 Jun Chen, Keisuke Nonaka, Ryosuke Watanabe, Hiroshi Sankoh, Houari Sabirin, Sei Naito

This paper proposes a new parallel approach to solve connected components on a 2D binary image implemented with CUDA.

An Optimized Union-Find Algorithm for Connected Components Labeling Using GPUs

no code implementations28 Aug 2017 Jun Chen, Qiang Yao, Houari Sabirin, Keisuke Nonaka, Hiroshi Sankoh, Sei Naito

In this paper, we report an optimized union-find (UF) algorithm that can label the connected components on a 2D image efficiently by employing the GPU architecture.

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