3 citations · 6 across the 7 of their papers we have counts for
7 papers
Lost in UNet: Improving Infrared Small Target Detection by Underappreciated Local Features
Wuzhou Quan, Wei Zhao, Weiming Wang +3
Many targets are often very small in infrared images due to the long-distance imaging meachnism. UNet and its variants, as popular detection backbone networks, downsample the local…
PointeNet: A Lightweight Framework for Effective and Efficient Point Cloud Analysis
Lipeng Gu, Xuefeng Yan, Liangliang Nan +4
Current methodologies in point cloud analysis predominantly explore 3D geometries, often achieved through the introduction of intricate learnable geometric extractors in the encode…
LBF:Learnable Bilateral Filter For Point Cloud Denoising
Huajian Si, Zeyong Wei, Zhe Zhu +4
Bilateral filter (BF) is a fast, lightweight and effective tool for image denoising and well extended to point cloud denoising. However, it often involves continual yet manual para…
PSFormer: Point Transformer for 3D Salient Object Detection
Baian Chen, Lipeng Gu, Xin Zhuang +3
We propose PSFormer, an effective point transformer model for 3D salient object detection. PSFormer is an encoder-decoder network that takes full advantage of transformers to model…
SPCNet: Stepwise Point Cloud Completion Network
Fei Hu, Honghua Chen, Xuequan Lu +5
How will you repair a physical object with large missings? You may first recover its global yet coarse shape and stepwise increase its local details. We are motivated to imitate th…
SO(3)-Pose: SO(3)-Equivariance Learning for 6D Object Pose Estimation
Haoran Pan, Jun Zhou, Yuanpeng Liu +4
6D pose estimation of rigid objects from RGB-D images is crucial for object grasping and manipulation in robotics. Although RGB channels and the depth (D) channel are often complem…
CF-YOLO: Cross Fusion YOLO for Object Detection in Adverse Weather with a High-quality Real Snow Dataset
Qiqi Ding, Peng Li, Xuefeng Yan +6
Snow is one of the toughest adverse weather conditions for object detection (OD). Currently, not only there is a lack of snowy OD datasets to train cutting-edge detectors, but also…