6 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…
Unsupervised Intrinsic Image Decomposition with LiDAR Intensity Enhanced Training
Shogo Sato, Takuhiro Kaneko, Kazuhiko Murasaki +3
Unsupervised intrinsic image decomposition (IID) is the process of separating a natural image into albedo and shade without these ground truths. A recent model employing light dete…