Enriching Point Clouds with Implicit Representations for 3D Classification and Segmentation

Continuous implicit representations can flexibly describe complex 3D geometry and offer excellent potential for 3D point cloud analysis. However, it remains challenging for existing point-based deep learning architectures to leverage the implicit representations due to the discrepancy in data structures between implicit fields and point...

Blind collision detection and classification framework for a heavyweight, low-speed mobile robot colliding with soft and hard objects

Abstract— Visual occlusion can cause object detection and classification systems to fail due to swift movement or a soiled, moist or dusty environment. Hence, this asks for a ’blind’ collision detection and classification method. This paper presents a novel blind collision detection and classification scheme for a<br/>heavyweight, low-speed,...

Semantic segmentation of mobile laser scanning point clouds with long short-term memory networks: Preliminary results

Although point clouds are characterized as a type of unstructured data, timestamp attribute can structure point clouds into scanlines and shape them into a time signal. The present work studies the transformation of the street point cloud into a time signal based on the Z component for the semantic segmentation using Long Short-Term Memory ...

Automating malaria diagnosis: a machine learning approach: Erythrocyte segmentation and parasite identification in thin blood smear microscopy images using convolutional neural networks

Reliable malaria diagnosis techniques that are suitable for point-of-care testing in high burden areas, are vital for effective treatment and monitoring of the disease. Identification of malaria parasites in Giemsa stained blood slides is currently the most widely accepted technique, but its availability is limited by the need for highly trained...

Automatic detection and characerization of ground occlusions in urban point clouds from mobile laser scanning data

Occlusions accompany serious problems that reduce the applicability of numerous algorithms. The aim of this work is to detect and characterize urban ground gaps based on occluding object. The point clouds for input have been acquired with Mobile Laser Scanning and have been previously segmented into ground, buildings and objects, which have...

Pose regression of 3D objects in monocular framework using a Convolutional Neural Network: Tracking multiple objects in real-time

In computer vision pose estimation of objects in everyday scenes is a basic need for a clearunderstanding of the surrounding environment, fields of interests include; augmented reality,surveillance, navigation, manipulation, and robotics in general. Pose estimation is a wellstudied topic, however fast and robust solutions are still hard to...