Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Jung, Dongki"'
Autor:
Lee, Yonghan, Choi, Jaehoon, Jung, Dongki, Yun, Jaeseong, Ryu, Soohyun, Manocha, Dinesh, Yeon, Suyong
We present a novel-view rendering algorithm, Mode-GS, for ground-robot trajectory datasets. Our approach is based on using anchored Gaussian splats, which are designed to overcome the limitations of existing 3D Gaussian splatting algorithms. Prior ne
Externí odkaz:
http://arxiv.org/abs/2410.04646
Autor:
Choi, Jaehoon, Jung, Dongki, Lee, Taejae, Kim, Sangwook, Jung, Youngdong, Manocha, Dinesh, Lee, Donghwan
We present a new pipeline for acquiring a textured mesh in the wild with a single smartphone which offers access to images, depth maps, and valid poses. Our method first introduces an RGBD-aided structure from motion, which can yield filtered depth m
Externí odkaz:
http://arxiv.org/abs/2303.15060
Monocular depth estimation in the wild inherently predicts depth up to an unknown scale. To resolve scale ambiguity issue, we present a learning algorithm that leverages monocular simultaneous localization and mapping (SLAM) with proprioceptive senso
Externí odkaz:
http://arxiv.org/abs/2203.05332
Autor:
Jung, Dongki, Choi, Jaehoon, Lee, Yonghan, Kim, Deokhwa, Kim, Changick, Manocha, Dinesh, Lee, Donghwan
We present a novel approach for estimating depth from a monocular camera as it moves through complex and crowded indoor environments, e.g., a department store or a metro station. Our approach predicts absolute scale depth maps over the entire scene c
Externí odkaz:
http://arxiv.org/abs/2108.05615
We present a novel algorithm for self-supervised monocular depth completion. Our approach is based on training a neural network that requires only sparse depth measurements and corresponding monocular video sequences without dense depth labels. Our s
Externí odkaz:
http://arxiv.org/abs/2011.04977
Style transfer is the image synthesis task, which applies a style of one image to another while preserving the content. In statistical methods, the adaptive instance normalization (AdaIN) whitens the source images and applies the style of target imag
Externí odkaz:
http://arxiv.org/abs/2010.02560
Self-supervised monocular depth estimation has emerged as a promising method because it does not require groundtruth depth maps during training. As an alternative for the groundtruth depth map, the photometric loss enables to provide self-supervision
Externí odkaz:
http://arxiv.org/abs/2010.02893
Partial domain adaptation (PDA), in which we assume the target label space is included in the source label space, is a general version of standard domain adaptation. Since the target label space is unknown, the main challenge of PDA is to reduce the
Externí odkaz:
http://arxiv.org/abs/2005.07858