5 papers
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…
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…
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…
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…
Memory-Efficient Point Cloud Registration via Overlapping Region Sampling
Tomoyasu Shimada, Kazuhiko Murasaki, Shogo Sato +3
Recent advances in deep learning have improved 3D point cloud registration but increased graphics processing unit (GPU) memory usage, often requiring preliminary sampling that redu…