A review on deep learning techniques applied to semantic segmentation
Image semantic segmentation is more and more being of interest for computer vision and
machine learning researchers. Many applications on the rise need accurate and efficient …
machine learning researchers. Many applications on the rise need accurate and efficient …
A survey on deep learning techniques for image and video semantic segmentation
Image semantic segmentation is more and more being of interest for computer vision and
machine learning researchers. Many applications on the rise need accurate and efficient …
machine learning researchers. Many applications on the rise need accurate and efficient …
Pointnext: Revisiting pointnet++ with improved training and scaling strategies
PointNet++ is one of the most influential neural architectures for point cloud understanding.
Although the accuracy of PointNet++ has been largely surpassed by recent networks such …
Although the accuracy of PointNet++ has been largely surpassed by recent networks such …
Point transformer
Self-attention networks have revolutionized natural language processing and are making
impressive strides in image analysis tasks such as image classification and object detection …
impressive strides in image analysis tasks such as image classification and object detection …
Pct: Point cloud transformer
The irregular domain and lack of ordering make it challenging to design deep neural
networks for point cloud processing. This paper presents a novel framework named Point …
networks for point cloud processing. This paper presents a novel framework named Point …
Point-bert: Pre-training 3d point cloud transformers with masked point modeling
We present Point-BERT, a novel paradigm for learning Transformers to generalize the
concept of BERT onto 3D point cloud. Following BERT, we devise a Masked Point Modeling …
concept of BERT onto 3D point cloud. Following BERT, we devise a Masked Point Modeling …
Masked autoencoders for point cloud self-supervised learning
As a promising scheme of self-supervised learning, masked autoencoding has significantly
advanced natural language processing and computer vision. Inspired by this, we propose a …
advanced natural language processing and computer vision. Inspired by this, we propose a …
Rethinking network design and local geometry in point cloud: A simple residual MLP framework
Point cloud analysis is challenging due to irregularity and unordered data structure. To
capture the 3D geometries, prior works mainly rely on exploring sophisticated local …
capture the 3D geometries, prior works mainly rely on exploring sophisticated local …
Dynamic graph cnn for learning on point clouds
Point clouds provide a flexible geometric representation suitable for countless applications
in computer graphics; they also comprise the raw output of most 3D data acquisition devices …
in computer graphics; they also comprise the raw output of most 3D data acquisition devices …
Kpconv: Flexible and deformable convolution for point clouds
Abstract We present Kernel Point Convolution (KPConv), a new design of point convolution,
ie that operates on point clouds without any intermediate representation. The convolution …
ie that operates on point clouds without any intermediate representation. The convolution …