Search Results for author: Qingyong Hu

Found 19 papers, 15 papers with code

Hierarchical Graph Transformer with Adaptive Node Sampling

1 code implementation8 Oct 2022 Zaixi Zhang, Qi Liu, Qingyong Hu, Chee-Kong Lee

The Transformer architecture has achieved remarkable success in a number of domains including natural language processing and computer vision.

DevNet: Self-supervised Monocular Depth Learning via Density Volume Construction

1 code implementation14 Sep 2022 Kaichen Zhou, Lanqing Hong, Changhao Chen, Hang Xu, Chaoqiang Ye, Qingyong Hu, Zhenguo Li

Self-supervised depth learning from monocular images normally relies on the 2D pixel-wise photometric relation between temporally adjacent image frames.

Depth Estimation

4DAC: Learning Attribute Compression for Dynamic Point Clouds

no code implementations25 Apr 2022 Guangchi Fang, Qingyong Hu, Yiling Xu, Yulan Guo

In addition, we also propose a deep conditional entropy model to estimate the probability distribution of the transformed coefficients, by incorporating temporal context from consecutive point clouds and the motion estimation/compensation modules.

Data Compression Motion Compensation +1

Meta-Sampler: Almost-Universal yet Task-Oriented Sampling for Point Clouds

no code implementations30 Mar 2022 Ta-Ying Cheng, Qingyong Hu, Qian Xie, Niki Trigoni, Andrew Markham

In this work, we propose an almost-universal sampler, in our quest for a sampler that can learn to preserve the most useful points for a particular task, yet be inexpensive to adapt to different tasks, models, or datasets.

Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds

1 code implementation CVPR 2022 Yifan Zhang, Qingyong Hu, Guoquan Xu, Yanxin Ma, Jianwei Wan, Yulan Guo

To reduce the memory and computational cost, existing point-based pipelines usually adopt task-agnostic random sampling or farthest point sampling to progressively downsample input point clouds, despite the fact that not all points are equally important to the task of object detection.

object-detection Object Detection

STPLS3D: A Large-Scale Synthetic and Real Aerial Photogrammetry 3D Point Cloud Dataset

4 code implementations17 Mar 2022 Meida Chen, Qingyong Hu, Zifan Yu, Hugues Thomas, Andrew Feng, Yu Hou, Kyle McCullough, Fengbo Ren, Lucio Soibelman

Specifically, we introduce a synthetic aerial photogrammetry point clouds generation pipeline that takes full advantage of open geospatial data sources and off-the-shelf commercial packages.

3D Instance Segmentation 3D Semantic Segmentation

3DAC: Learning Attribute Compression for Point Clouds

1 code implementation CVPR 2022 Guangchi Fang, Qingyong Hu, Hanyun Wang, Yiling Xu, Yulan Guo

Finally, the estimated probabilities are used to further compress these transform coefficients to a final attributes bitstream.

SensatUrban: Learning Semantics from Urban-Scale Photogrammetric Point Clouds

no code implementations12 Jan 2022 Qingyong Hu, Bo Yang, Sheikh Khalid, Wen Xiao, Niki Trigoni, Andrew Markham

Each point in the dataset has been labelled with fine-grained semantic annotations, resulting in a dataset that is three times the size of the previous existing largest photogrammetric point cloud dataset.

Box2Seg: Learning Semantics of 3D Point Clouds with Box-Level Supervision

no code implementations9 Jan 2022 Yan Liu, Qingyong Hu, Yinjie Lei, Kai Xu, Jonathan Li, Yulan Guo

In this paper, we introduce a neural architecture, termed Box2Seg, to learn point-level semantics of 3D point clouds with bounding box-level supervision.

Semantic Segmentation

Detecting and Tracking Small and Dense Moving Objects in Satellite Videos: A Benchmark

1 code implementation25 Nov 2021 Qian Yin, Qingyong Hu, Hao liu, Feng Zhang, Yingqian Wang, Zaiping Lin, Wei An, Yulan Guo

Satellite video cameras can provide continuous observation for a large-scale area, which is important for many remote sensing applications.

Matrix Completion Moving Object Detection +2

Towards Semantic Segmentation of Urban-Scale 3D Point Clouds: A Dataset, Benchmarks and Challenges

2 code implementations CVPR 2021 Qingyong Hu, Bo Yang, Sheikh Khalid, Wen Xiao, Niki Trigoni, Andrew Markham

An essential prerequisite for unleashing the potential of supervised deep learning algorithms in the area of 3D scene understanding is the availability of large-scale and richly annotated datasets.

Scene Understanding Semantic Segmentation

Axiom-based Grad-CAM: Towards Accurate Visualization and Explanation of CNNs

3 code implementations5 Aug 2020 Ruigang Fu, Qingyong Hu, Xiaohu Dong, Yulan Guo, Yinghui Gao, Biao Li

To have a better understanding and usage of Convolution Neural Networks (CNNs), the visualization and interpretation of CNNs has attracted increasing attention in recent years.

Image Generation

Deep Learning for 3D Point Clouds: A Survey

3 code implementations27 Dec 2019 Yulan Guo, Hanyun Wang, Qingyong Hu, Hao liu, Li Liu, Mohammed Bennamoun

To stimulate future research, this paper presents a comprehensive review of recent progress in deep learning methods for point clouds.

3D Object Detection 3D Shape Classification +3

Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds

1 code implementation NeurIPS 2019 Bo Yang, Jianan Wang, Ronald Clark, Qingyong Hu, Sen Wang, Andrew Markham, Niki Trigoni

The framework directly regresses 3D bounding boxes for all instances in a point cloud, while simultaneously predicting a point-level mask for each instance.

Ranked #11 on 3D Instance Segmentation on S3DIS (mPrec metric)

3D Instance Segmentation Philosophy +1

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