Method for 3D modelling based on structure from motion processing of sparse 2D images

A method based on Structure from Motion for processing a plurality of sparse images acquired by one or more acquisition devices to generate a sparse 3D points cloud and of a plurality of internal and external parameters of the acquisition devices includes the steps of collecting the images; extracting keypoints therefrom and generating keypoint descriptors; organizing the images in a proximity graph; pairwise image matching and generating keypoints connecting tracks according maximum proximity between keypoints; performing an autocalibration between image clusters to extract internal and external parameters of the acquisition devices, wherein calibration groups are defined that contain a plurality of image clusters and wherein a clustering algorithm iteratively merges the clusters in a model expressed in a common local reference system starting from clusters belonging to the same calibration group; and performing a Euclidean reconstruction of the object as a sparse 3D point cloud based on the extracted parameters

Accelerated volumetric reconstruction from uncalibrated camera views

While both work with images, computer graphics and computer vision are inverse problems. Computer graphics starts traditionally with input geometric models and produces image sequences. Computer vision starts with input image sequences and produces geometric models. In the last few years, there has been a convergence of research to bridge the gap between the two fields. This convergence has produced a new field called Image-based Rendering and Modeling (IBMR). IBMR represents the effort of using the geometric information recovered from real images to generate new images with the hope that the synthesized ones appear photorealistic, as well as reducing the time spent on model creation. In this dissertation, the capturing, geometric and photometric aspects of an IBMR system are studied. A versatile framework was developed that enables the reconstruction of scenes from images acquired with a handheld digital camera. The proposed system targets applications in areas such as Computer Gaming and Virtual Reality, from a lowcost perspective. In the spirit of IBMR, the human operator is allowed to provide the high-level information, while underlying algorithms are used to perform low-level computational work. Conforming to the latest architecture trends, we propose a streaming voxel carving method, allowing a fast GPU-based processing on commodity hardware

Relating Multimodal Imagery Data in 3D

This research develops and improves the fundamental mathematical approaches and techniques required to relate imagery and imagery derived multimodal products in 3D. Image registration, in a 2D sense, will always be limited by the 3D effects of viewing geometry on the target. Therefore, effects such as occlusion, parallax, shadowing, and terrain/building elevation can often be mitigated with even a modest amounts of 3D target modeling. Additionally, the imaged scene may appear radically different based on the sensed modality of interest; this is evident from the differences in visible, infrared, polarimetric, and radar imagery of the same site. This thesis develops a `model-centric\u27 approach to relating multimodal imagery in a 3D environment. By correctly modeling a site of interest, both geometrically and physically, it is possible to remove/mitigate some of the most difficult challenges associated with multimodal image registration. In order to accomplish this feat, the mathematical framework necessary to relate imagery to geometric models is thoroughly examined. Since geometric models may need to be generated to apply this `model-centric\u27 approach, this research develops methods to derive 3D models from imagery and LIDAR data. Of critical note, is the implementation of complimentary techniques for relating multimodal imagery that utilize the geometric model in concert with physics based modeling to simulate scene appearance under diverse imaging scenarios. Finally, the often neglected final phase of mapping localized image registration results back to the world coordinate system model for final data archival are addressed. In short, once a target site is properly modeled, both geometrically and physically, it is possible to orient the 3D model to the same viewing perspective as a captured image to enable proper registration. If done accurately, the synthetic model\u27s physical appearance can simulate the imaged modality of interest while simultaneously removing the 3-D ambiguity between the model and the captured image. Once registered, the captured image can then be archived as a texture map on the geometric site model. In this way, the 3D information that was lost when the image was acquired can be regained and properly related with other datasets for data fusion and analysis

Dense Point Cloud Extraction From Oblique Imagery

With the increasing availability of low-cost digital cameras with small or medium sized sensors, more and more airborne images are available with high resolution, which enhances the possibility in establishing three dimensional models for urban areas. The high accuracy of representation of buildings in urban areas is required for asset valuation or disaster recovery. Many automatic methods for modeling and reconstruction are applied to aerial images together with Light Detection and Ranging (LiDAR) data. If LiDAR data are not provided, manual steps must be applied, which results in semi-automated technique. The automated extraction of 3D urban models can be aided by the automatic extraction of dense point clouds. The more dense the point clouds, the easier the modeling and the higher the accuracy. Also oblique aerial imagery provides more facade information than nadir images, such as building height and texture. So a method for automatic dense point cloud extraction from oblique images is desired. In this thesis, a modified workflow for the automated extraction of dense point clouds from oblique images is proposed and tested. The result reveals that this modified workflow works well and a very dense point cloud can be extracted from only two oblique images with slightly higher accuracy in flat areas than the one extracted by the original workflow. The original workflow was established by previous research at the Rochester Institute of Technology (RIT) for point cloud extraction from nadir images. For oblique images, a first modification is proposed in the feature detection part by replacing the Scale-Invariant Feature Transform (SIFT) algorithm with the Affine Scale-Invariant Feature Transform (ASIFT) algorithm. After that, in order to realize a very dense point cloud, the Semi-Global Matching (SGM) algorithm is implemented in the second modification to compute the disparity map from a stereo image pair, which can then be used to reproject pixels back to a point cloud. A noise removal step is added in the third modification. The point cloud from the modified workflow is much denser compared to the result from the original workflow. An accuracy assessment is made in the end to evaluate the point cloud extracted from the modified workflow. From the two flat areas, subsets of points are selected from both original and modified workflow, and then planes are fitted to them, respectively. The Mean Squared Error (MSE) of the points to the fitted plane is compared. The point subsets from the modified workflow have slightly lower MSEs than the ones from the original workflow, respectively. This suggests a much more dense and more accurate point cloud can lead to clear roof borders for roof extraction and improve the possibility of 3D feature detection for 3D point cloud registration