Zobrazeno 1 - 10
of 103
pro vyhledávání: '"Im, Sunghoon"'
Understanding the motion states of the surrounding environment is critical for safe autonomous driving. These motion states can be accurately derived from scene flow, which captures the three-dimensional motion field of points. Existing LiDAR scene f
Externí odkaz:
http://arxiv.org/abs/2407.07995
In this paper, we introduce the Context-Aware Video Instance Segmentation (CAVIS), a novel framework designed to enhance instance association by integrating contextual information adjacent to each object. To efficiently extract and leverage this info
Externí odkaz:
http://arxiv.org/abs/2407.03010
We propose a practical approach to JPEG image decoding, utilizing a local implicit neural representation with continuous cosine formulation. The JPEG algorithm significantly quantizes discrete cosine transform (DCT) spectra to achieve a high compress
Externí odkaz:
http://arxiv.org/abs/2404.05558
Autor:
Moghaddam, Mahsa Haddadi, Wang, Zhihao, Dalayoan, Daryll J. C, Park, Daehwan, Kim, Hwanhee, Im, Sunghoon, Ji, Kyungbin, Kang, Daeshik, Das, Bamadev, Kim, Dai Sik
Metal thin films on soft polymers provide a unique opportunity for resistance-based strain sensors. A mechanical mismatch between the conductive film and the flexible substrate causes cracks to open and close, changing the electrical resistance as a
Externí odkaz:
http://arxiv.org/abs/2403.08288
Real-time processing is crucial in autonomous driving systems due to the imperative of instantaneous decision-making and rapid response. In real-world scenarios, autonomous vehicles are continuously tasked with interpreting their surroundings, analyz
Externí odkaz:
http://arxiv.org/abs/2403.03468
Monocular 3D object detection poses a significant challenge due to the lack of depth information in RGB images. Many existing methods strive to enhance the object depth estimation performance by allocating additional parameters for object depth estim
Externí odkaz:
http://arxiv.org/abs/2401.01075
In the realm of LiDAR-based perception, significant strides have been made, yet domain generalization remains a substantial challenge. The performance often deteriorates when models are applied to unfamiliar datasets with different LiDAR sensors or d
Externí odkaz:
http://arxiv.org/abs/2312.12098
Research on monocular 3D object detection is being actively studied, and as a result, performance has been steadily improving. However, 3D object detection performance is significantly reduced when applied to a camera system different from the system
Externí odkaz:
http://arxiv.org/abs/2310.05366
Existing frameworks for image stitching often provide visually reasonable stitchings. However, they suffer from blurry artifacts and disparities in illumination, depth level, etc. Although the recent learning-based stitchings relax such disparities,
Externí odkaz:
http://arxiv.org/abs/2309.01409
Autor:
Choi, Wonhyeok, Im, Sunghoon
In this paper, we present a new MTL framework that searches for structures optimized for multiple tasks with diverse graph topologies and shares features among tasks. We design a restricted DAG-based central network with read-in/read-out layers to bu
Externí odkaz:
http://arxiv.org/abs/2303.06856