1 citations · 1 across the 6 of their papers we have counts for
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LagrangeGS: Non-Conservative Lagrangian System on Dynamic 3D Gaussian Splatting
Shogo Sato, Takuhiro Kaneko, Shoichiro Takeda +4
Dynamic 3D Gaussian Splatting (3DGS) achieves photorealistic reconstruction of time-varying scenes, and recent physics-aware extensions improve extrapolation by explicitly predicti…
Probing Association Instability with Track-State Perturbations for Clip-Level Active Learning in Query-Propagation Multi-Object Tracking
Riku Inoue, Shogo Sato, Kazuhiko Murasaki +3
Training query-propagation end-to-end multi-object tracking (MOT) models requires dense bounding-box and identity annotations across video sequences, making dataset construction ex…
Clip-level Uncertainty and Temporal-aware Active Learning for End-to-End Multi-Object Tracking
Riku Inoue, Shogo Sato, Kazuhiko Murasaki +3
Multi-Object Tracking (MOT) in dynamic environments relies on robust temporal reasoning to maintain consistent object identities over time. Transformer-based end-to-end MOT models…
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…
Unsupervised Intrinsic Image Decomposition with LiDAR Intensity
Shogo Sato, Yasuhiro Yao, Taiga Yoshida +3
Intrinsic image decomposition (IID) is the task that decomposes a natural image into albedo and shade. While IID is typically solved through supervised learning methods, it is not…