Patch-based Progressive 3D Point Set Upsampling

We present a detail-driven deep neural network for point set upsampling. A high-resolution point set is essential for point-based rendering and surface reconstruction. Inspired by the recent success of neural image super-resolution techniques, we progressively train a cascade of patch-based upsampling networks on different levels of detail end-to-end. We propose a series of architectural design contributions that lead to a substantial performance boost. The effect of each technical contribution is demonstrated in an ablation study. Qualitative and quantitative experiments show that our method significantly outperforms the state-of-the-art learning-based and optimazation-based approaches, both in terms of handling low-resolution inputs and revealing high-fidelity details.Comment: accepted to cvpr2019, code available at https://github.com/yifita/P3

MeshLab

MeshLab the open source system for processing and editing 3D triangular meshes. It provides a set of tools for editing, cleaning, healing, inspecting, rendering, texturing and converting meshes. It offers features for processing raw data produced by 3D digitization tools/devices and for preparing models for 3D printing. With over 2 millions download, MeshLab is a de-fact standard tool in for mesh processing

The use of 3D laser scanning technology in buildings archaeology: the case of Måketorpsboden in Kulturen museum, Lund

This thesis is a project developed with Kulturen Museum in Lund for the documentation of a wooden building from the end of the 18th century. The technology applied is 3D laser scanning. The project wanted to answer several theoretical questions through the study and to conduct a practical case analysis, which lead to the production of a 3D textured model of part of the building. The work has been developed discussing in the beginning the state of the art of building archaeology, describing the different stages of study of buildings through history, from Renaissance to the seventeenth century and with an overlook of the approach of this discipline in the Nordic countries. Subsequently it has analysed the different kind of surveys for a proper archaeological building investigation: the direct survey and the indirect survey, explaining the differences and the technological innovation applied to this field especially during the last 20 years. A detailed paragraph about method of building investigation with the most recent laser scanning technologies illustrates the “pros and cons” of the utilization of 3D laser scanning for archaeological purposes; specific case studies are described. A final comment about the rising problem of handling and storing of data coming from the utilization of those new technologies has been taken in consideration. The last part of the paper is focused on the explanation of the background history of the typology of wooden building I have been studying and the detailed explanation of all the steps done for the actual project, from the acquisition campaign to the post processing of the data. An analysis of the data I got from the creation of the model of a single room has been performed with the examination of the possibility of future developments of the same project

Rescan: Inductive Instance Segmentation for Indoor RGBD Scans

In depth-sensing applications ranging from home robotics to AR/VR, it will be common to acquire 3D scans of interior spaces repeatedly at sparse time intervals (e.g., as part of regular daily use). We propose an algorithm that analyzes these "rescans" to infer a temporal model of a scene with semantic instance information. Our algorithm operates inductively by using the temporal model resulting from past observations to infer an instance segmentation of a new scan, which is then used to update the temporal model. The model contains object instance associations across time and thus can be used to track individual objects, even though there are only sparse observations. During experiments with a new benchmark for the new task, our algorithm outperforms alternate approaches based on state-of-the-art networks for semantic instance segmentation.Comment: IEEE International Conference on Computer Vision 201