1 code implementation • 7 May 2020 • Richard Sahala Hartanto, Ryoichi Ishikawa, Menandro Roxas, Takeshi Oishi
In this paper, we present a method for simultaneous articulation model estimation and segmentation of an articulated object in RGB-D images using human hand motion.
1 code implementation • 28 Nov 2023 • Wooseok Kim, Taiki Fukiage, Takeshi Oishi
However, when handling scenes with multiple glass objects, e. g., objects in a glass showcase, modeling the target scene accurately has been challenging due to the presence of multiple reflection and refraction effects.
1 code implementation • 13 Apr 2018 • Ryoichi Ishikawa, Takeshi Oishi, Katsushi Ikeuchi
In the experiments, we confirm the parameters obtained by two types of offline calibration according to the degree of freedom of robot movement and validate the effectiveness of online correction method by plotting localized position error during robot's intense movement.
no code implementations • 14 Apr 2018 • Ryoichi Ishikawa, Takeshi Oishi, Katsushi Ikeuchi
In this paper, we propose a method of targetless and automatic Camera-LiDAR calibration.
no code implementations • 30 Jul 2017 • Menandro Roxas, Tomoki Hori, Taiki Fukiage, Yasuhide Okamoto, Takeshi Oishi
Real-time occlusion handling is a major problem in outdoor mixed reality system because it requires great computational cost mainly due to the complexity of the scene.
no code implementations • 17 Sep 2019 • Menandro Roxas, Takeshi Oishi
We also propose a fast way of generating the trajectory field without increasing the processing time compared to conventional rectified methods.
no code implementations • 25 Jun 2020 • Yasuhiro Yao, Menandro Roxas, Ryoichi Ishikawa, Shingo Ando, Jun Shimamura, Takeshi Oishi
Our experiments show that our method can outperform previous unsupervised and semi-supervised depth completion methods in terms of accuracy.
no code implementations • 13 Nov 2020 • Jan Hausberg, Ryoichi Ishikawa, Menandro Roxas, Takeshi Oishi
We propose a dynamically adaptive kernel-based method for drone detection and tracking using the LiDAR.
no code implementations • 23 Mar 2021 • Daichi Kawakami, Ryoichi Ishikawa, Menandro Roxas, Yoshihiro Sato, Takeshi Oishi
As the number of the robot's degrees of freedom increases, the implementation of robot motion becomes more complex and difficult.
no code implementations • 4 Oct 2022 • Yasuhiro Yao, Ryoichi Ishikawa, Shingo Ando, Kana Kurata, Naoki Ito, Jun Shimamura, Takeshi Oishi
Moreover, under various LiDAR-camera calibration errors, the proposed method reduced the depth estimation MAE to 0. 34-0. 93 times from previous depth completion methods.
no code implementations • 28 Aug 2023 • Shuyi Zhou, Shuxiang Xie, Ryoichi Ishikawa, Ken Sakurada, Masaki Onishi, Takeshi Oishi
INF first trains a neural density field of the target scene using LiDAR frames.
no code implementations • 27 Feb 2024 • Lian Fu, Ryoichi Ishikawa, Yoshihiro Sato, Takeshi Oishi
CAPT uses an end-to-end transformer-based architecture for joint parameter and state estimation of articulated objects from a single point cloud.