no code implementations • 29 Apr 2024 • Siyuan Xiang, Chin Tseng, Congcong Wen, Deshana Desai, Yifeng Kou, Binil Starly, Daniele Panozzo, Chen Feng
We introduce the first work on benchmarking and evaluating deep clustering algorithms on large-scale non-categorical 3D CAD models.
no code implementations • 14 Feb 2024 • Congcong Wen, Jiazhao Liang, Shuaihang Yuan, Hao Huang, Yi Fang
In the field of robotics and automation, navigation systems based on Large Language Models (LLMs) have recently shown impressive performance.
no code implementations • 27 Sep 2023 • Wenyu Han, Congcong Wen, Lazarus Chok, Yan Liang Tan, Sheung Lung Chan, Hang Zhao, Chen Feng
Based on this dataset, we propose AETree, a tree-structured auto-encoder neural network, for city generation.
1 code implementation • 28 Jul 2023 • Yuan Hu, Jianlong Yuan, Congcong Wen, Xiaonan Lu, Xiang Li
This dataset consists of human-annotated captions and visual question-answer pairs, allowing for a comprehensive assessment of VLMs in the context of RS.
2 code implementations • 9 May 2023 • Xiang Li, Congcong Wen, Yuan Hu, Zhenghang Yuan, Xiao Xiang Zhu
Existing AI-related research in remote sensing primarily focuses on visual understanding tasks while neglecting the semantic understanding of the objects and their relationships.
1 code implementation • ICCV 2021 • Yiming Li, Congcong Wen, Felix Juefei-Xu, Chen Feng
LiDAR point clouds collected from a moving vehicle are functions of its trajectories, because the sensor motion needs to be compensated to avoid distortions.
no code implementations • 1 Jan 2021 • Congcong Wen, Wenyu Han, Hang Zhao, Chen Feng
Areal spatial data represent not only geographical locations but also sizes and shapes of physical objects such as buildings in a city.
no code implementations • 20 Apr 2020 • Congcong Wen, Xiang Li, Xiaojing Yao, Ling Peng, Tianhe Chi
To achieve point cloud classification, previous studies proposed point cloud deep learning models that can directly process raw point clouds based on PointNet-like architectures.
no code implementations • 14 Oct 2019 • Xiang Li, Mingyang Wang, Congcong Wen, Lingjing Wang, Nan Zhou, Yi Fang
Based on this convolution module, we further developed a multi-scale fully convolutional neural network with downsampling and upsampling blocks to enable hierarchical point feature learning.
1 code implementation • 19 Aug 2019 • Congcong Wen, Lina Yang, Ling Peng, Xiang Li, Tianhe Chi
In this paper, we proposed a directionally constrained fully convolutional neural network (D-FCN) that can take the original 3D coordinates and LiDAR intensity as input; thus, it can directly apply to unstructured 3D point clouds for semantic labeling.