Hyunmin Kang

Moving-target detection under strict sensing constraints is a recurring subproblem in surveillance, search-and-rescue, and autonomous robotics. We study a canonical one-dimensional finite grid in which a sensor probes one location per time step with binary observations while the target follows reflecting random-walk dynamics. The objective is to minimize the expected time to detection using transparent, training-free decision rules defined on the belief state of the target location. We compare two belief-driven heuristics with purely online implementation: a greedy rule that always probes the most probable location and a belief-proportional sampling (BPS, probability matching) rule that samples sensing locations according to the belief distribution (i.e., posterior probability of the target location). Repeated Monte Carlo simulations quantify the exploitation–exploration trade-off and provide a self-comparison between the two policies. Across tested grid sizes, the greedy policy consistently yields the shortest expected time to detection, improving by roughly 17–20% over BPS and uniform random probing in representative settings. BPS trades some average efficiency for stochastic exploration, which can be beneficial under model mismatch. This study provides an interpretable baseline and quantitative reference for extensions to noisy sensing and higher-dimensional search.

A holographic stereogram displays reconstructed 3D images by rearranging multiple 2D viewpoint images into small holographic pixels (hogels). However, conventional CPU-based hogel generation processes these images sequentially, causing computation times to soar with as the resolution and number of viewpoints increase, which makes real-time implementation difficult. In this study, we introduce a GPU-accelerated parallel processing method to speed up the generation of high-resolution hogel images and achieve near-real-time performance. Specifically, we implement the pixel-rearrangement algorithm for multiple viewpoint images as a CUDA-based GPU kernel, designing it so that thousands of threads process individual pixels simultaneously. We also optimize CPU–GPU data transfers and improve memory access efficiency to maximize GPU parallel performance. The experimental results show that the proposed method achieves over a 5× speedup compared to the CPU across resolutions from FHD to 8K while maintaining output image quality equivalent to that of the CPU approach. Notably, we confirm near-real-time performance by processing large-scale 8K resolution with 16 viewpoints in just tens of milliseconds. This achievement significantly alleviates the computational bottleneck in large-scale holographic image synthesis, bringing real-time 3D holographic displays one step closer to realization. Furthermore, the proposed GPU acceleration technique is expected to serve as a foundational technology for real-time high-resolution hogel image generation in next-generation immersive display devices such as AR/VR/XR.

A calibration algorithm for a system comprising a camera and turntable is provided, in which the camera captures three-dimensional point clouds while a target object rotates on the turntable to three-dimensionally scan the entire object. A mathematical approach is used to obtain the geometry between the coordinate systems of the camera and the turntable. As a minimal setup, the camera captures two views of a chessboard calibration pattern placed on the turntable. The coordinate transformation from the camera to the turntable is then calculated through geometric reasoning. First, two camera poses are computed with respect to the chessboard images taken before and after rotation. The direction of the rotation axis of the turntable coordinate system can be calculated without any ambiguity. However, there is one degree of freedom in the choice of the other two directions and one degree of freedom in choosing the location of the origin of the turntable coordinate system. We provide a practical method for these choices. Experimental results with a stereo-vision system and a turntable validate the proposed method.

객체의 3차원 점군을 정합하기 위한 대표적 방법인 ICP(iterative closet point)알고리즘은 최소 자승법을 사용하여 다른 두 시점의 점군 사이의 강체 변환을 추정한다. 그러나 이 방법은 목적함수의 수렴을 위하여 적절한 초기 솔루션이 필요하거나 검색 중에 목적 함수가 지역적 최소점(local minima)에 갇히는 경우에는 만족스럽지 못한 정합 결과를 초래할 수 있다. 그래서 본 논문에서는 객체의 3차원 모델을 구축하기 위하여 기하 정합의 성능뿐만 아니라 계산 복잡도도 고려하기 위하여 회전 테이블의 수학적 분석에 의해 고안된 기하 보정을 사용함으로써 물리적으로 검색 공간을 줄이는 방법을 제시한다. 이 방법은 구조광 시스템과 회전 테이블 사이에 기하학적 변환이 있음을 증명하고, 회전 테이블의 좌표로 변환된 점군의 정합을 1차원 회전 변환만을 통해 신속하게 수행 할 수 있음을 보여준다.

구조광 시스템 및 TOF 방식의 레이저와 같은 기술은 객체의 형상을 얻기 위한 능동적인 방법으로써 강건하고 정교한 3차원 기하 정보를 제공한다. 이러한 방법들은 대응점의 신뢰성이 보장된다는 이점을 갖는다. 일반적으로 구조광 시스템은 객체에 대한 적절한 기하 정보를 얻을 수 있지만, 프로젝터의 해상도 제한으로 인한 미세한 형상에 대하여 평균화되거나 수평 해상도가 감소되는 경향이 있다. 이 연구에서 형상 복원의 정확도를 개선하기 위하여 다중 카메라와 다수의 프로젝터를 이용하는 초고해상도 방법을 제안한다. 제안하는 방법은 통계적 분석을 통하여 객체 표면에 대하여 다수의 다른 시점의 프로젝터 패턴이 투사, 중첩되면서 객체표면의 최소 영역을 고유하게 식별 할 수 있도록 한다. 따라서 높은 밀도를 갖는 정확한 3차원 점군을 획득 할 수 있다.