This work proposes a method bridging the existing gap between progressive sparse 3D reconstruction (incremental Structure from Motion) and progressive point based dense 3D reconstruction (Multi-View Stereo). The presented algorithm is capable of adapting an existing dense 3D model to changes such as the addition or removal of new images, the merge of scene parts, or changes in the underlying camera calibration. The existing 3D model is transformed as consistently as possible and the structure is reused as much as possible without sacrificing the accuracy and/or completeness of the final result. A significant decrease in runtime is achieved compared to the re-computation of a new dense point cloud from scratch. We demonstrate the performance of the algorithm in various experiments on publicly available datasets of different sizes and compare it to the baseline. The work interacts seamlessly with publicly available software enabling an integrated progressive 3D modeling pipeline.

This work proposes a progressive patch based multiview stereo algorithm able to deliver a dense point cloud at any time. This enables an immediate feedback on the reconstruction process in a user centric scenario. With increasing processing time, the model is improved in terms of resolution and accuracy. The algorithm explicitly handles input images with varying effective scale and creates visually pleasing point clouds. A priority scheme assures that the limited computational power is invested in scene parts, where the user is most interested in or the overall error can be reduced the most. The architecture of the proposed pipeline allows fast processing times in large scenes using a pure open-source CPU implementation. We show the performance of our algorithm on challenging standard datasets as well as on real-world scenes and compare it to the baseline.

Recently, Structure-from-Motion pipelines (SfM) for the 3D reconstruction of scenes from images were pushed from desktop computers onto mobile devices, like phones or tablets. However, mobile devices offer much more than just necessary computational power. A combination of handheld device with camera, display and full connectivity entails possibilities for an on-line 3D reconstruction that would have been difficult to implement otherwise. In this work, we propose a combination of a regular mobile phone as frontend with a centralized server plus annex cloud as backend for collaborative, on-line 3D reconstruction. We illustrate few advantages of this combination of a myriad of new possibilities: First, we automatically balance computational load between the frontend and the backend depending on battery autonomy and available bandwidth. Second, we select the best of algorithms given the available resources to obtain better 3D models. Finally, we allow for collaborative modeling in order to arrive at more complete and more detailed models, especially when the objects or scenes are big. This paper presents an implementation of such a joint mobile-cloud modeling approach and demonstrates its advantages via real-life reconstructions.

Many methods for 3D reconstruction of deformable surfaces from a monocular view rely on inextensibility constraints. An interesting application with commercial potential lies in augmented reality in portable and wearable devices. Such applications add an additional challenge to the 3D reconstruction, since in portable platforms the availability of resources is limited and not always guaranteed. Towards this goal, we introduce a method to deliver the best possible 3D reconstruction of the deformable surface at any time. Since computational resources may vary, it is decided on-the-fly when to stop the reconstruction algorithm. We use an efficient optimization method to quickly deliver the reconstructed surface. We introduce bootstrapping to improve the robustness of the efficient 3D reconstruction algorithm by merging multiple versions of the reconstructed surface. Also, these multiple 3D surfaces can be used to estimate the confidence of the reconstruction. In a series of experiments, in both synthetic and real data, we show that our method is effective for timely reconstruction of 3D surfaces.