이주찬 교수

Neural Radiance Fields (NeRFs) have demonstrated re-markable potential in capturing complex 3D scenes with high fidelity. However, one persistent challenge that hin-ders the widespread adoption of NeRFs is the computational bottleneck due to the volumetric rendering. On the other hand, 3D Gaussian splatting (3DGS) has recently emerged as an alternative representation that leverages a 3D Gaussisan-based representation and adopts the ras-terization pipeline to render the images rather than volumet

Neural fields, also known as coordinate-based or implicit neural representations, have shown a remarkable capability of representing, generating, and manipulating various forms of signals. For video representations, however, mapping pixel-wise coordinates to RGB colors has shown relatively low compression performance and slow convergence and inference speed. Frame-wise video representation, which maps a temporal coordinate to its entire frame, has recently emerged as an alternative method to rep

While recent advances in deep neural networks (DNNs) enabled remarkable performance on various computer vision tasks, it is challenging for edge devices to perform real-time inference of complex DNN models due to their stringent resource constraint. To enhance the inference throughput, recent studies proposed collaborative intelligence (CI) that splits DNN computation into edge and cloud platforms, mostly for simple tasks such as image classification. However, for general DNN-based object detect

Although recent advances in deep neural networks (DNNs) have enabled remarkable performance on various computer vision tasks, it is challenging for edge devices to perform real-time inference of complex DNN models due to their stringent resource constraints. To enhance the inference throughput, recent studies have proposed collaborative intelligence (CI), which splits DNN computation into edge and cloud platforms, mostly for simple tasks, such as image classification. However, for general DNN-ba

Abstract Aerial images obtained from autonomous aerial vehicles have lots of small and densely distributed objects because of the capture distance. This paper proposes a deep neural network architecture and training/inference techniques for robust detection of objects in the aerial images. Based on cascade R‐CNN, the proposed model adopts the recursive feature pyramid and switchable atrous convolution for robust detection of dense objects. A patch‐level division and multi‐scale inference techniq

3D Gaussian splatting (3DGS) has recently emerged as an alternative representation that leverages a 3D Gaussian-based representation and introduces an approximated volumetric rendering, achieving very fast rendering speed and promising image quality. Furthermore, subsequent studies have successfully extended 3DGS to dynamic 3D scenes, demonstrating its wide range of applications. However, a significant drawback arises as 3DGS and its following methods entail a substantial number of Gaussians to

Neural Radiance Fields (NeRFs) have demonstrated remarkable potential in capturing complex 3D scenes with high fidelity. However, one persistent challenge that hinders the widespread adoption of NeRFs is the computational bottleneck due to the volumetric rendering. On the other hand, 3D Gaussian splatting (3DGS) has recently emerged as an alternative representation that leverages a 3D Gaussisan-based representation and adopts the rasterization pipeline to render the images rather than volumetric

In recent years, 1 a large number of studies have been conducted on cybersecurity for Programmable Logic Controllers (PLCs) to cope with cyberattacks on Industrial Control Systems(ICS). However, few studies have been conducted on ensuring cybersafety for control logics running inside PLCs. In this study, a technique for detecting an attack on PLC control logic change was proposed by analyzing the network protocol data and project file structure. Based on the analysis results for the proposed tec

Neural fields, also known as coordinate-based or implicit neural representations, have shown a remarkable capability of representing, generating, and manipulating various forms of signals. For video representations, however, mapping pixel-wise coordinates to RGB colors has shown relatively low compression performance and slow convergence and inference speed. Frame-wise video representation, which maps a temporal coordinate to its entire frame, has recently emerged as an alternative method to rep

3D Gaussian Splatting (3DGS) has emerged as a powerful representation for real-time, high-performance rendering, enabling a wide range of applications. However, representing 3D scenes with numerous explicit Gaussian primitives imposes significant storage and memory overhead. Recent studies have shown that high-quality rendering can be achieved with a substantially reduced number of Gaussians when represented with high-precision attributes. Nevertheless, existing 3DGS compression methods still re

Neural fields, mapping low-dimensional input coordinates to corresponding signals, have shown promising results in representing various signals. Numerous methodologies have been proposed, and techniques employing MLPs and grid representations have achieved substantial success. MLPs allow compact and high expressibility, yet often suffer from spectral bias and slow convergence speed. On the other hand, methods using grids are free from spectral bias and achieve fast training speed, however, at th

There have been significant advancements in anomaly detection in an unsupervised manner, where only normal images are available for training. Several recent methods aim to detect anomalies based on a memory, comparing or reconstructing the input with directly stored normal features (or trained features with normal images). However, such memory-based approaches operate on a discrete feature space implemented by the nearest neighbor or attention mechanism, suffering from poor generalization or an