Search Results for author: Takeshi Oishi

Found 12 papers, 3 papers with code

Occlusion Handling using Semantic Segmentation and Visibility-Based Rendering for Mixed Reality

no code implementations30 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.

Depth Estimation Mixed Reality +4

Offline and Online calibration of Mobile Robot and SLAM Device for Navigation

1 code implementation13 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.

Mixed Reality Position +1

Real-Time Variational Fisheye Stereo without Rectification and Undistortion

no code implementations17 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.

Autonomous Driving Stereo Matching +1

A Hand Motion-guided Articulation and Segmentation Estimation

1 code implementation7 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.

Object

Discontinuous and Smooth Depth Completion with Binary Anisotropic Diffusion Tensor

no code implementations25 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.

Depth Completion

Learning 6DoF Grasping Using Reward-Consistent Demonstration

no code implementations23 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.

Imitation Learning reinforcement-learning +1

Non-learning Stereo-aided Depth Completion under Mis-projection via Selective Stereo Matching

no code implementations4 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.

Camera Calibration Depth Completion +2

REF$^2$-NeRF: Reflection and Refraction aware Neural Radiance Field

1 code implementation28 Nov 2023 Wooseok Kim, Taiki Fukiage, Takeshi Oishi

Recently, significant progress has been made in the study of methods for 3D reconstruction from multiple images using implicit neural representations, exemplified by the neural radiance field (NeRF) method.

3D Reconstruction

CAPT: Category-level Articulation Estimation from a Single Point Cloud Using Transformer

no code implementations27 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.

Cannot find the paper you are looking for? You can Submit a new open access paper.