arXiv subjects

Yuichi Hiroi

Publications and source records attributed to Yuichi Hiroi.

22 records · Page 2Linked to original sources

arXiv2024

Measurement of the Imperceptible Threshold for Color Vibration Pairs Selected by using MacAdam Ellipse

We propose an efficient method for searching for color vibration pairs that are imperceptible to the human eye based on the MacAdam ellipse, an experimentally determined color-difference range that is indistinguishable to the human eye. We created color pairs by selecting eight colors within the sRGB color space specified by the ellipse, and conducted experiments to confirm the threshold of the amplitude of color vibration amplitude at which flicker becomes imperceptible to the human eye. The experimental results indicate a general guideline for acceptable amplitudes for pair selection.

cs.HC↗

arXiv2024

Telextiles: End-to-end Remote Transmission of Fabric Tactile Sensation

The tactile sensation of textiles is critical in determining the comfort of clothing. For remote use, such as online shopping, users cannot physically touch the textile of clothes, making it difficult to evaluate its tactile sensation. Tactile sensing and actuation devices are required to transmit the tactile sensation of textiles. The sensing device needs to recognize different garments, even with hand-held sensors. In addition, the existing actuation device can only present a limited number of known patterns and cannot transmit unknown tactile sensations of textiles. To address these issues, we propose Telextiles, an interface that can remotely transmit tactile sensations of textiles by creating a latent space that reflects the proximity of textiles through contrastive self-supervised learning. We confirm that textiles with similar tactile features are located close to each other in the latent space through a two-dimensional plot. We then compress the latent features for known textile samples into the 1D distance and apply the 16 textile samples to the rollers in the order of the distance. The roller is rotated to select the textile with the closest feature if an unknown textile is detected.

cs.HC↗

arXiv2022

NeARportation: A Remote Real-time Neural Rendering Framework

While the presentation of photo-realistic appearance plays a major role in immersion in an augmented virtuality environment, displaying the photo-realistic appearance of real objects remains a challenging problem. Recent developments in photogrammetry have facilitated the incorporation of real objects into virtual space. However, photo-realistic photogrammetry requires a dedicated measurement environment, and there is a trade-off between measurement cost and quality. Furthermore, even with photo-realistic appearance measurements, there is a trade-off between rendering quality and framerate. There is no framework that could resolve these trade-offs and easily provide a photo-realistic appearance in real-time. Our NeARportation framework combines server-client bidirectional communication and neural rendering to resolve these trade-offs. Neural rendering on the server receives the client's head posture and generates a novel-view image with realistic appearance reproduction, which is streamed onto the client's display. By applying our framework to a stereoscopic display, we confirmed that it could display a high-fidelity appearance on full-HD stereo videos at 35-40 frames-per-second (fps), according to the user's head motion.

cs.HC↗

arXiv2022

Neural Distortion Fields for Spatial Calibration of Wide Field-of-View Near-Eye Displays

We propose a spatial calibration method for wide Field-of-View (FoV) Near-Eye Displays (NEDs) with complex image distortions. Image distortions in NEDs can destroy the reality of the virtual object and cause sickness. To achieve distortion-free images in NEDs, it is necessary to establish a pixel-by-pixel correspondence between the viewpoint and the displayed image. Designing compact and wide-FoV NEDs requires complex optical designs. In such designs, the displayed images are subject to gaze-contingent, non-linear geometric distortions, which explicit geometric models can be difficult to represent or computationally intensive to optimize. To solve these problems, we propose Neural Distortion Field (NDF), a fully-connected deep neural network that implicitly represents display surfaces complexly distorted in spaces. NDF takes spatial position and gaze direction as input and outputs the display pixel coordinate and its intensity as perceived in the input gaze direction. We synthesize the distortion map from a novel viewpoint by querying points on the ray from the viewpoint and computing a weighted sum to project output display coordinates into an image. Experiments showed that NDF calibrates an augmented reality NED with 90$^{\circ}$ FoV with about 3.23 pixel (5.8 arcmin) median error using only 8 training viewpoints. Additionally, we confirmed that NDF calibrates more accurately than the non-linear polynomial fitting, especially around the center of the FoV.

cs.CV↗