10 papers
Learning Stable In-Grasp Manipulation in a Non-Dropping Action Space
Ha Thang Long Doan, Hikaru Arita, Kazuto Nakashima +1
Traditionally, dexterous manipulation controllers are designed using analytic models constrained by strong assumptions about the hand and the objects being manipulated. Reinforceme…
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…
Fast LiDAR Upsampling using Conditional Diffusion Models
Sander Elias Magnussen Helgesen, Kazuto Nakashima, Jim Tørresen +1
The search for refining 3D LiDAR data has attracted growing interest motivated by recent techniques such as supervised learning or generative model-based methods. Existing approach…
LiDAR Data Synthesis with Denoising Diffusion Probabilistic Models
Kazuto Nakashima, Ryo Kurazume
Generative modeling of 3D LiDAR data is an emerging task with promising applications for autonomous mobile robots, such as scalable simulation, scene manipulation, and sparse-to-de…
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…
Learning to Drop Points for LiDAR Scan Synthesis
Kazuto Nakashima, Ryo Kurazume
3D laser scanning by LiDAR sensors plays an important role for mobile robots to understand their surroundings. Nevertheless, not all systems have high resolution and accuracy due t…