no code implementations • 19 May 2023 • Qiong Chang, Xiang Li, Xin Xu, Xin Liu, Yun Li, Miyazaki Jun
We present a lightweight system for stereo matching through embedded GPUs.
no code implementations • 1 Dec 2022 • Qiong Chang, Tsutomu Maruyama
In this paper, we propose a low error rate and real-time stereo vision system on GPU.
no code implementations • 1 Dec 2022 • Qiong Chang, Aolong Zha, Weimin WANG, Xin Liu, Masaki Onishi, Lei Lei, Meng Joo Er, Tsutomu Maruyama
By combining this technique with the domain transformation (DT) algorithm, our system show real-time processing speed of 32 fps, on a Jetson Tx2 GPU for 1, 280x384 pixel images with a maximum disparity of 128.
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 #4 on 3D Part Segmentation on ShapeNet-Part
1 code implementation • Database and Expert Systems Applications 2022 • Yun Liu, Jun Miyazaki, Qiong Chang
In the proposed framework, we use an attention-based multi-hop propagation mechanism to take users and movies as center nodes and extend their attributes along with the connections of the knowledge graph by recursively calculating the different contributions of their neighbors.
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.