PhD Defense by Tolga Birdal
|
Geometric Methods for 3D Reconstruction from Large Point Clouds
Abstract:3D reconstruction involves the task of capturing the shape and appearance of objects or scenes in the form of 3D computer aided design (CAD) models, often through multiple measurements: individual 2D images or multiview 3D data. In this thesis, we explore both sparse and dense 3D reconstruction methods in scenarios where 3D cues are present and rough, prior CAD models are at hand before the operation. Thanks to the proliferation of 3D sensors and increased accessibility of 3D data, we can now remain true to the 3D nature of our physical world in such digitization processes and utilize direct 3D input, point clouds, which can also alleviate problems of capture modality and illumination conditions.
Both sparse and dense reconstruction problems arise frequently either in industrial machine vision where the production processes of parts and goods are to be inspected, or in restoration applications where crude digital models are desired to be improved. We start by explaining our contributions to sparse yet accurate reconstruction from non-overlapping multiview images. The experience developed here have also been used to acquire accurate ground truth aiding the assessment of the next stage, dense reconstruction.We tackle the latter by proposing a novel, multiview point cloud based 3D reconstruction pipeline in which it is possible to incorporate CAD proxies. This is accomplished by first aligning all the scans not by an O(N2) inter-scan matching but by a linear scan-to-model registration. Such alignment is made possible by novel object detection and pose estimation algorithms.Next, respecting the deviations of the real data from the CAD model, we perform a CAD-free multi-scan refinement further increasing the accuracy both qualitatively and quantitatively. We also propose novel methods to initialize such large scale optimization problems and to infer information about the reliability of solutions, known as the uncertainty.
As seen, the necessity to incorporate the CAD models as proxies to reconstruction comes along with many challenges to be addressed. In this thesis, the prominent subtasks include preparing CAD models towards the reconstruction task, object detection, estimation of full six degree of freedom (DoF) rigid pose and pose graph optimization, in which the roughly aligned scans are brought to the final alignment. We address all of those problems with rigor.Moreover, thanks to our photogrammetric ground truth acquisition strategies, we present thorough evaluation of all tasks, in real datasets besides synthetic ones.
| WebEventForm | |
|---|---|
| Title: | Geometric Methods for 3D Reconstruction from Large Point Clouds |
| Date: | 19 December 2018 |
| Location: | 00.12.019 |
| Abstract: | 3D reconstruction involves the task of capturing the shape and appearance of objects or scenes in the formof 3D computer aided design (CAD) models, often throughmultiple measurements: individual 2D images or multiview 3D data. In this thesis, we explore both sparse and dense 3D reconstruction methods in scenarios where 3D cues are present and rough, prior CAD models are at hand before the operation. Thanks to the proliferation of 3D sensors and increased accessibility of 3D data, we can now remain true to the 3D nature of our physical world in such digitization processes and utilize direct 3D input, point clouds, which can also alleviate problems of capture modality and illumination conditions. Both sparse and dense reconstruction problems arise frequently either in industrial machine vision where the production processes of parts and goods are to be inspected, or in restoration applications where crude digital models are desired to be improved. We start by explaining our contributions to sparse yet accurate reconstruction from non-overlapping multiview images. The experience developed here have also been used to acquire accurate ground truth aiding the assessment of the next stage, dense reconstruction.We tackle the latter by proposing a novel, multiview point cloud based 3D reconstruction pipeline in which it is possible to incorporate CAD proxies. This is accomplished by first aligning all the scans not by an O(N2) inter-scan matching but by a linear scan-to-model registration. Such alignment ismade possible by novel object detection and pose estimation algorithms.Next, respecting the deviations of the real data from the CAD model, we performa CAD-free multi-scan refinement further increasing the accuracy both qualitatively and quantitatively.We also propose novel methods to initialize such large scale optimization problems and to infer information about the reliability of solutions, known as the uncertainty. As seen, the necessity to incorporate the CAD models as proxies to reconstruction comes along withmany challenges to be addressed. In this thesis, the prominent subtasks include preparing CAD models towards the reconstruction task, object detection, estimation of full six degree of freedom (DoF?) rigid pose and pose graph optimization, in which the roughly aligned scans are brought to the final alignment.We address all of those problems with rigor.Moreover, thanks to our photogrammetric ground truth acquisition strategies, we present thorough evaluation of all tasks, in real datasets besides synthetic ones. |
| Imageurl: |  |
| Type: | News |
| Videourl: | |
| Conferencelink: | |
