19 citations · 39 across the 10 of their papers we have counts for
7 papers · 1 filter
Stereo-LiDAR Fusion by Semi-Global Matching With Discrete Disparity-Matching Cost and Semidensification
Yasuhiro Yao, Ryoichi Ishikawa, Takeshi Oishi
We present a real-time, non-learning depth estimation method that fuses Light Detection and Ranging (LiDAR) data with stereo camera input. Our approach comprises three key techniqu…
CAPT: Category-level Articulation Estimation from a Single Point Cloud Using Transformer
Lian Fu, Ryoichi Ishikawa, Yoshihiro Sato +1
The ability to estimate joint parameters is essential for various applications in robotics and computer vision. In this paper, we propose CAPT: category-level articulation estimati…
INF: Implicit Neural Fusion for LiDAR and Camera
Shuyi Zhou, Shuxiang Xie, Ryoichi Ishikawa +3
Sensor fusion has become a popular topic in robotics. However, conventional fusion methods encounter many difficulties, such as data representation differences, sensor variations,…
Non-learning Stereo-aided Depth Completion under Mis-projection via Selective Stereo Matching
Yasuhiro Yao, Ryoichi Ishikawa, Shingo Ando +4
We propose a non-learning depth completion method for a sparse depth map captured using a light detection and ranging (LiDAR) sensor guided by a pair of stereo images. Generally, c…
Discontinuous and Smooth Depth Completion with Binary Anisotropic Diffusion Tensor
Yasuhiro Yao, Menandro Roxas, Ryoichi Ishikawa +3
We propose an unsupervised real-time dense depth completion from a sparse depth map guided by a single image. Our method generates a smooth depth map while preserving discontinuity…
LiDAR and Camera Calibration using Motion Estimated by Sensor Fusion Odometry
Ryoichi Ishikawa, Takeshi Oishi, Katsushi Ikeuchi
In this paper, we propose a method of targetless and automatic Camera-LiDAR calibration. Our approach is an extension of hand-eye calibration framework to 2D-3D calibration. By usi…