We introduce a novel technique for neural point cloud consolidation which learns from only the input cloud. Unlike other up-sampling methods analyze shapes via local patches, in this work, we learn global subsets. repeatedly self-sample with subsets that are used to train deep network. Specifically, define source and target according desired criteria (e.g., generating sharp points or sparse reg...

This work presents a method for retrieving 3D building contours usable in facade retrofitting projects, which uses parametric modeling workflow that utilizes point-cloud slicing to retrieve such contours. Since current commitments by European governments seek reduce energy consumption as means carbon emissions from stock 2050, appears an alternative addressing operational and embedded emissions...

With the rapid development of reality capture methods, such as laser scanning and oblique photogrammetry, point cloud data have become third most important source, after vector maps imagery. Point also play an increasingly role in scientific research engineering fields Earth science, spatial cognition, smart cities. However, how to acquire high-quality three-dimensional (3D) geospatial informat...

This paper presents an automated and effective framework for classifying airborne laser scanning (ALS) point clouds. The framework is composed of four stages: (i) step-wise point cloud segmentation, (ii) feature extraction, (iii) Random Forests (RF) based feature selection and classification, and (iv) post-processing. First, a step-wise point cloud segmentation method is proposed to extract thr...