Search Results for author:
Found 9 papers, 2 papers with code
Dynamic Clustering Transformer Network for Point Cloud Segmentation
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.
NeRF: Neural Radiance Field in 3D Vision, A Comprehensive Review
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.
3DGTN: 3D Dual-Attention GLocal Transformer Network for Point Cloud Classification and Segmentation
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.
3DLG-Detector: 3D Object Detection via Simultaneous Local-Global Feature Learning
Capturing both local and global features of irregular point clouds is essential to 3D object detection (3OD).
MODNet: Multi-offset Point Cloud Denoising Network Customized for Multi-scale Patches
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.
Transformers in 3D Point Clouds: A Survey
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.
3DCTN: 3D Convolution-Transformer Network for Point Cloud Classification
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.
VENet: Voting Enhancement Network for 3D Object Detection
Hough voting, as has been demonstrated in VoteNet, is effective for 3D object detection, where voting is a key step.
Deep Feature-preserving Normal Estimation for Point Cloud Filtering
In this paper, we propose a novel feature-preserving normal estimation method for point cloud filtering with preserving geometric features.
