1 code implementation • 4 Aug 2023 • Haoyi Xiu, Xin Liu, Weimin WANG, Kyoung-Sook Kim, Masashi Matsuoka
MSECNet consists of a backbone network and a multi-scale edge conditioning (MSEC) stream.
1 code implementation • 20 Sep 2022 • Haoyi Xiu, Xin Liu, Weimin WANG, Kyoung-Sook Kim, Takayuki Shinohara, Qiong Chang, Masashi Matsuoka
Second, we experimentally observe and verify the edge enhancement and suppression behavior.
Ranked #3 on 3D Part Segmentation on ShapeNet-Part
no code implementations • 4 Jul 2022 • Haoyi Xiu, Xin Liu, Weimin WANG, Kyoung-Sook Kim, Takayuki Shinohara, Qiong Chang, Masashi Matsuoka
Modeling the local surface geometry is challenging in 3D point cloud understanding due to the lack of connectivity information.
no code implementations • 4 Jul 2022 • Haoyi Xiu, Xin Liu, Weimin WANG, Kyoung-Sook Kim, Takayuki Shinohara, Qiong Chang, Masashi Matsuoka
Learning point clouds is challenging due to the lack of connectivity information, i. e., edges.
no code implementations • 1 Mar 2022 • Haoyi Xiu, Xin Liu, Weimin WANG, Kyoung-Sook Kim, Takayuki Shinohara, Qiong Chang, Masashi Matsuoka
We present a simple but effective attention named the unary-pairwise attention (UPA) for modeling the relationship between 3D point clouds.
no code implementations • 14 Sep 2020 • Bruno Adriano, Naoto Yokoya, Junshi Xia, Hiroyuki Miura, Wen Liu, Masashi Matsuoka, Shunichi Koshimura
In this study, we have developed a global multisensor and multitemporal dataset for building damage mapping.
no code implementations • 13 Oct 2017 • Kenji Enomoto, Ken Sakurada, Weimin WANG, Hiroshi Fukui, Masashi Matsuoka, Ryosuke Nakamura, Nobuo Kawaguchi
The networks are trained to output images that are close to the ground truth using the images synthesized with clouds over the ground truth as inputs.
Ranked #8 on Cloud Removal on SEN12MS-CR