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of 22
pro vyhledávání: '"Choe, Jaesung"'
Autor:
Joung, Byeongin, Lee, Byeong-Uk, Choe, Jaesung, Shin, Ukcheol, Kang, Minjun, Lee, Taeyeop, Kweon, In So, Yoon, Kuk-Jin
This paper proposes an algorithm for synthesizing novel views under few-shot setup. The main concept is to develop a stable surface regularization technique called Annealing Signed Distance Function (ASDF), which anneals the surface in a coarse-to-fi
Externí odkaz:
http://arxiv.org/abs/2403.19985
Autor:
Mirza, M. Jehanzeb, Shin, Inkyu, Lin, Wei, Schriebl, Andreas, Sun, Kunyang, Choe, Jaesung, Possegger, Horst, Kozinski, Mateusz, Kweon, In So, Yoon, Kun-Jin, Bischof, Horst
Our MATE is the first Test-Time-Training (TTT) method designed for 3D data, which makes deep networks trained for point cloud classification robust to distribution shifts occurring in test data. Like existing TTT methods from the 2D image domain, MAT
Externí odkaz:
http://arxiv.org/abs/2211.11432
Autor:
Kang, Minjun, Choe, Jaesung, Ha, Hyowon, Jeon, Hae-Gon, Im, Sunghoon, Kweon, In So, Yoon, KuK-Jin
Many mobile manufacturers recently have adopted Dual-Pixel (DP) sensors in their flagship models for faster auto-focus and aesthetic image captures. Despite their advantages, research on their usage for 3D facial understanding has been limited due to
Externí odkaz:
http://arxiv.org/abs/2111.12928
Autor:
Lee, Taeyeop, Lee, Byeong-Uk, Shin, Inkyu, Choe, Jaesung, Shin, Ukcheol, Kweon, In So, Yoon, Kuk-Jin
Learning to estimate object pose often requires ground-truth (GT) labels, such as CAD model and absolute-scale object pose, which is expensive and laborious to obtain in the real world. To tackle this problem, we propose an unsupervised domain adapta
Externí odkaz:
http://arxiv.org/abs/2111.12580
Point cloud obtained from 3D scanning is often sparse, noisy, and irregular. To cope with these issues, recent studies have been separately conducted to densify, denoise, and complete inaccurate point cloud. In this paper, we advocate that jointly so
Externí odkaz:
http://arxiv.org/abs/2111.11704
MLP-Mixer has newly appeared as a new challenger against the realm of CNNs and transformer. Despite its simplicity compared to transformer, the concept of channel-mixing MLPs and token-mixing MLPs achieves noticeable performance in visual recognition
Externí odkaz:
http://arxiv.org/abs/2111.11187
To reconstruct a 3D scene from a set of calibrated views, traditional multi-view stereo techniques rely on two distinct stages: local depth maps computation and global depth maps fusion. Recent studies concentrate on deep neural architectures for dep
Externí odkaz:
http://arxiv.org/abs/2108.08623
Stereo-LiDAR fusion is a promising task in that we can utilize two different types of 3D perceptions for practical usage -- dense 3D information (stereo cameras) and highly-accurate sparse point clouds (LiDAR). However, due to their different modalit
Externí odkaz:
http://arxiv.org/abs/2103.12964
This paper presents a stereo object matching method that exploits both 2D contextual information from images as well as 3D object-level information. Unlike existing stereo matching methods that exclusively focus on the pixel-level correspondence betw
Externí odkaz:
http://arxiv.org/abs/2103.12498
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