Point cloud learning has lately attracted increasing attention due to its wide applications in
many areas, such as computer vision, autonomous driving, and robotics. As a dominating …

Deep learning approach has been used extensively in image analysis tasks. However,
implementing the methods in 3D data is a bit complex because most of the previously …

We present a simple and general framework for feature learning from point cloud. The key to
the success of CNNs is the convolution operator that is capable of leveraging spatially-local …

Point clouds are unstructured and unordered data, as opposed to images. Thus, most
machine learning approach developed for image cannot be directly transferred to point …

Point cloud is an important type of 3D representation. However, directly applying
convolutions on point clouds is challenging due to the sparse, irregular and unordered data …

The accurate plant organ segmentation is crucial and challenging to the quantification of
plant architecture and selection of plant ideotype. The popularity of point cloud data and …

3D data is a valuable asset the computer vision filed as it provides rich information about the
full geometry of sensed objects and scenes. Recently, with the availability of both large 3D …

Exploiting fine-grained semantic features on point cloud data is still challenging because of
its irregular and sparse structure in a non-Euclidean space. In order to represent the local …

Automation in point cloud data processing is central in knowledge discovery within decision-
making systems. The definition of relevant features is often key for segmentation and …

Convolution on 3D point clouds is widely researched yet far from perfect in geometric deep
learning. The traditional wisdom of convolution characterises feature correspondences …