collaborators

6 papers

cs.CV2026

Real-Time LiDAR Point Cloud Densification for Low-Latency Spatial Data Transmission

Kazuhiko Murasaki, Shunsuke Konagai, Masakatsu Aoki +2

To realize low-latency spatial transmission system for immersive telepresence, there are two major problems: capturing dynamic 3D scene densely and processing them in real time. Li…

cs.CV2026

Leveraging 2D-VLM for Label-Free 3D Segmentation in Large-Scale Outdoor Scene Understanding

Toshihiko Nishimura, Hirofumi Abe, Kazuhiko Murasaki +2

This paper presents a novel 3D semantic segmentation method for large-scale point cloud data that does not require annotated 3D training data or paired RGB images. The proposed app…

cs.CV2025

IPCD: Intrinsic Point-Cloud Decomposition

Shogo Sato, Takuhiro Kaneko, Shoichiro Takeda +5

Point clouds are widely used in various fields, including augmented reality (AR) and robotics, where relighting and texture editing are crucial for realistic visualization. Achievi…

cs.CV2025

Objective, Absolute and Hue-aware Metrics for Intrinsic Image Decomposition on Real-World Scenes: A Proof of Concept

Shogo Sato, Masaru Tsuchida, Mariko Yamaguchi +4

Intrinsic image decomposition (IID) is the task of separating an image into albedo and shade. In real-world scenes, it is difficult to quantitatively assess IID quality due to the…

cs.CV2025

LiM-Loc: Visual Localization with Dense and Accurate 3D Reference Maps Directly Corresponding 2D Keypoints to 3D LiDAR Point Clouds

Masahiko Tsuji, Hitoshi Niigaki, Ryuichi Tanida

Visual localization is to estimate the 6-DOF camera pose of a query image in a 3D reference map. We extract keypoints from the reference image and generate a 3D reference map with…

cs.CV2025

MultiBARF: Integrating Imagery of Different Wavelength Regions by Using Neural Radiance Fields

Kana Kurata, Hitoshi Niigaki, Xiaojun Wu +1

Optical sensor applications have become popular through digital transformation. Linking observed data to real-world locations and combining different image sensors is essential to…