no code implementations • 30 May 2023 • Dening Lu, Jun Zhou, Kyle Yilin Gao, Dilong Li, Jing Du, Linlin Xu, Jonathan Li
Specifically, we propose novel semantic feature-based dynamic sampling and clustering methods in the encoder, which enables the model to be aware of local semantic homogeneity for local feature aggregation.
no code implementations • 1 Oct 2022 • Kyle Gao, Yina Gao, Hongjie He, Dening Lu, Linlin Xu, Jonathan Li
Neural Radiance Field (NeRF) has recently become a significant development in the field of Computer Vision, allowing for implicit, neural network-based scene representation and novel view synthesis.
no code implementations • 21 Sep 2022 • Dening Lu, Kyle Gao, Qian Xie, Linlin Xu, Jonathan Li
This paper presents a novel point cloud representational learning network, called 3D Dual Self-attention Global Local (GLocal) Transformer Network (3DGTN), for improved feature learning in both classification and segmentation tasks, with the following key contributions.
no code implementations • 31 Aug 2022 • Baian Chen, Liangliang Nan, Haoran Xie, Dening Lu, Fu Lee Wang, Mingqiang Wei
Capturing both local and global features of irregular point clouds is essential to 3D object detection (3OD).
1 code implementation • 30 Aug 2022 • Anyi Huang, Qian Xie, Zhoutao Wang, Dening Lu, Mingqiang Wei, Jun Wang
Second, a multi-scale perception module is designed to embed multi-scale geometric information for each scale feature and regress multi-scale weights to guide a multi-offset denoising displacement.
no code implementations • 16 May 2022 • Dening Lu, Qian Xie, Mingqiang Wei, Kyle Gao, Linlin Xu, Jonathan Li
To demonstrate the superiority of Transformers in point cloud analysis, we present comprehensive comparisons of various Transformer-based methods for classification, segmentation, and object detection.
1 code implementation • 2 Mar 2022 • Dening Lu, Qian Xie, Linlin Xu, Jonathan Li
This paper presents a novel hierarchical framework that incorporates convolution with Transformer for point cloud classification, named 3D Convolution-Transformer Network (3DCTN), to combine the strong and efficient local feature learning ability of convolution with the remarkable global context modeling capability of Transformer.
no code implementations • ICCV 2021 • Qian Xie, Yu-Kun Lai, Jing Wu, Zhoutao Wang, Dening Lu, Mingqiang Wei, Jun Wang
Hough voting, as has been demonstrated in VoteNet, is effective for 3D object detection, where voting is a key step.
no code implementations • 24 Apr 2020 • Dening Lu, Xuequan Lu, Yangxing Sun, Jun Wang
In this paper, we propose a novel feature-preserving normal estimation method for point cloud filtering with preserving geometric features.