16 citations · 25 across the 9 of their papers we have counts for
9 papers
Multi-modal panoramic 3D outdoor datasets for place categorization
Hojung Jung, Yuki Oto, Oscar M. Mozos +2
We present two multi-modal panoramic 3D outdoor (MPO) datasets for semantic place categorization with six categories: forest, coast, residential area, urban area and indoor/outdoor…
DRUM: Diffusion-based Raydrop-aware Unpaired Mapping for Sim2Real LiDAR Segmentation
Tomoya Miyawaki, Kazuto Nakashima, Yumi Iwashita +1
LiDAR-based semantic segmentation is a key component for autonomous mobile robots, yet large-scale annotation of LiDAR point clouds is prohibitively expensive and time-consuming. A…
Learning Geometric and Photometric Features from Panoramic LiDAR Scans for Outdoor Place Categorization
Kazuto Nakashima, Hojung Jung, Yuki Oto +3
Semantic place categorization, which is one of the essential tasks for autonomous robots and vehicles, allows them to have capabilities of self-decision and navigation in unfamilia…
Enhancing the Quality of 3D Lunar Maps Using JAXA's Kaguya Imagery
Yumi Iwashita, Haakon Moe, Yang Cheng +6
As global efforts to explore the Moon intensify, the need for high-quality 3D lunar maps becomes increasingly critical-particularly for long-distance missions such as NASA's Endura…
Fast LiDAR Data Generation with Rectified Flows
Kazuto Nakashima, Xiaowen Liu, Tomoya Miyawaki +2
Building LiDAR generative models holds promise as powerful data priors for restoration, scene manipulation, and scalable simulation in autonomous mobile robots. In recent years, ap…
Gait Sequence Upsampling using Diffusion Models for Single LiDAR Sensors
Jeongho Ahn, Kazuto Nakashima, Koki Yoshino +2
Recently, 3D LiDAR has emerged as a promising technique in the field of gait-based person identification, serving as an alternative to traditional RGB cameras, due to its robustnes…
Generative Range Imaging for Learning Scene Priors of 3D LiDAR Data
Kazuto Nakashima, Yumi Iwashita, Ryo Kurazume
3D LiDAR sensors are indispensable for the robust vision of autonomous mobile robots. However, deploying LiDAR-based perception algorithms often fails due to a domain gap from the…
A clustering approach to time series forecasting using neural networks: A comparative study on distance-based vs. feature-based clustering methods
Manie Tadayon, Yumi Iwashita
Time series forecasting has gained lots of attention recently; this is because many real-world phenomena can be modeled as time series. The massive volume of data and recent advanc…
Single Sample Feature Importance: An Interpretable Algorithm for Low-Level Feature Analysis
Joseph Gatto, Ravi Lanka, Yumi Iwashita +1
Have you ever wondered how your feature space is impacting the prediction of a specific sample in your dataset? In this paper, we introduce Single Sample Feature Importance (SSFI),…