Trifocal Relative Pose from Lines at Points and its Efficient Solution

We present a new minimal problem for relative pose estimation mixing point features with lines incident at points observed in three views and its efficient homotopy continuation solver. We demonstrate the generality of the approach by analyzing and solving an additional problem with mixed point and line correspondences in three views. The minimal problems include correspondences of (i) three points and one line and (ii) three points and two lines through two of the points which is reported and analyzed here for the first time. These are difficult to solve, as they have 216 and - as shown here - 312 solutions, but cover important practical situations when line and point features appear together, e.g., in urban scenes or when observing curves. We demonstrate that even such difficult problems can be solved robustly using a suitable homotopy continuation technique and we provide an implementation optimized for minimal problems that can be integrated into engineering applications. Our simulated and real experiments demonstrate our solvers in the camera geometry computation task in structure from motion. We show that new solvers allow for reconstructing challenging scenes where the standard two-view initialization of structure from motion fails.Comment: This material is based upon work supported by the National Science Foundation under Grant No. DMS-1439786 while most authors were in residence at Brown University's Institute for Computational and Experimental Research in Mathematics -- ICERM, in Providence, R

Reconstruction of 3D Points From Uncalibrated Underwater Video

This thesis presents a 3D reconstruction software pipeline that is capable of generating point cloud data from uncalibrated underwater video. This research project was undertaken as a partnership with 2G Robotics, and the pipeline described in this thesis will become the 3D reconstruction engine for a software product that can generate photo-realistic 3D models from underwater video. The pipeline proceeds in three stages: video tracking, projective reconstruction, and autocalibration. Video tracking serves two functions: tracking recognizable feature points, as well as selecting well-spaced keyframes with a wide enough baseline to be used in the reconstruction. Video tracking is accomplished using Lucas-Kanade optical flow as implemented in the OpenCV toolkit. This simple and widely used method is well-suited to underwater video, which is taken by carefully piloted and slow-moving underwater vehicles. Projective reconstruction is the process of simultaneously calculating the motion of the cameras and the 3D location of observed points in the scene. This is accomplished using a geometric three-view technique. Results are presented showing that the projective reconstruction algorithm detailed here compares favourably to state-of-the-art methods. Autocalibration is the process of transforming a projective reconstruction, which is not suitable for visualization or measurement, into a metric space where it can be used. This is the most challenging part of the 3D reconstruction pipeline, and this thesis presents a novel autocalibration algorithm. Results are shown for two existing cost function-based methods in the literature which failed when applied to underwater video, as well as the proposed hybrid method. The hybrid method combines the best parts of its two parent methods, and produces good results on underwater video. Final results are shown for the 3D reconstruction pipeline operating on short under- water video sequences to produce visually accurate 3D point clouds of the scene, suitable for photorealistic rendering. Although further work remains to extend and improve the pipeline for operation on longer sequences, this thesis presents a proof-of-concept method for 3D reconstruction from uncalibrated underwater video

In-loop Feature Tracking for Structure and Motion with Out-of-core Optimization

In this paper, a novel and approach for obtaining 3D models from video sequences captured with hand-held cameras is addressed. We define a pipeline that robustly deals with different types of sequences and acquiring devices. Our system follows a divide and conquer approach: after a frame decimation that pre-conditions the input sequence, the video is split into short-length clips. This allows to parallelize the reconstruction step which translates into a reduction in the amount of computational resources required. The short length of the clips allows an intensive search for the best solution at each step of reconstruction which robustifies the system. The process of feature tracking is embedded within the reconstruction loop for each clip as opposed to other approaches. A final registration step, merges all the processed clips to the same coordinate fram

Hierarchical structure-and-motion recovery from uncalibrated images

This paper addresses the structure-and-motion problem, that requires to find camera motion and 3D struc- ture from point matches. A new pipeline, dubbed Samantha, is presented, that departs from the prevailing sequential paradigm and embraces instead a hierarchical approach. This method has several advantages, like a provably lower computational complexity, which is necessary to achieve true scalability, and better error containment, leading to more stability and less drift. Moreover, a practical autocalibration procedure allows to process images without ancillary information. Experiments with real data assess the accuracy and the computational efficiency of the method.Comment: Accepted for publication in CVI

Non-Parametric Sequential Frame Decimation for Scene Reconstruction in Low-Memory Streaming Environments

Abstract not provide

Reconstruction quasi-dense et modèles 3D à partir d'une séquence d'images

National audienceCe papier propose une reconstruction quasi-dense à partir d'une séquence d'images non calibrées ainsi qu'un système associé de reconstruction de modèles 3D. La principale innovation est que toute la géométrie est calculée à partir de mises en correspondances quasi-denses sous-échantillonnées au lieu des points d'intérets épars usuels. Cela produit non seulement une reconstruction plus précise (au sens des incertitudes) et plus robuste grace à des mises en correspondances bien redondantes et réparties dans les images, mais aussi une reconstruction plus adéquate (car plus dense) pour l'application de la reconstruction de surface. Des expériences sur des séquences réelles montrent de meilleures performances des reconstructions quasi-denses par rapport aux reconstructions éparses à la fois en robustesse et incertitudes. De plus, les surfaces des nombreux objets ont été obtenues à partir des points quasi-denses reconstruits

Image Based View Synthesis

This dissertation deals with the image-based approach to synthesize a virtual scene using sparse images or a video sequence without the use of 3D models. In our scenario, a real dynamic or static scene is captured by a set of un-calibrated images from different viewpoints. After automatically recovering the geometric transformations between these images, a series of photo-realistic virtual views can be rendered and a virtual environment covered by these several static cameras can be synthesized. This image-based approach has applications in object recognition, object transfer, video synthesis and video compression. In this dissertation, I have contributed to several sub-problems related to image based view synthesis. Before image-based view synthesis can be performed, images need to be segmented into individual objects. Assuming that a scene can approximately be described by multiple planar regions, I have developed a robust and novel approach to automatically extract a set of affine or projective transformations induced by these regions, correctly detect the occlusion pixels over multiple consecutive frames, and accurately segment the scene into several motion layers. First, a number of seed regions using correspondences in two frames are determined, and the seed regions are expanded and outliers are rejected employing the graph cuts method integrated with level set representation. Next, these initial regions are merged into several initial layers according to the motion similarity. Third, the occlusion order constraints on multiple frames are explored, which guarantee that the occlusion area increases with the temporal order in a short period and effectively maintains segmentation consistency over multiple consecutive frames. Then the correct layer segmentation is obtained by using a graph cuts algorithm, and the occlusions between the overlapping layers are explicitly determined. Several experimental results are demonstrated to show that our approach is effective and robust. Recovering the geometrical transformations among images of a scene is a prerequisite step for image-based view synthesis. I have developed a wide baseline matching algorithm to identify the correspondences between two un-calibrated images, and to further determine the geometric relationship between images, such as epipolar geometry or projective transformation. In our approach, a set of salient features, edge-corners, are detected to provide robust and consistent matching primitives. Then, based on the Singular Value Decomposition (SVD) of an affine matrix, we effectively quantize the search space into two independent subspaces for rotation angle and scaling factor, and then we use a two-stage affine matching algorithm to obtain robust matches between these two frames. The experimental results on a number of wide baseline images strongly demonstrate that our matching method outperforms the state-of-art algorithms even under the significant camera motion, illumination variation, occlusion, and self-similarity. Given the wide baseline matches among images I have developed a novel method for Dynamic view morphing. Dynamic view morphing deals with the scenes containing moving objects in presence of camera motion. The objects can be rigid or non-rigid, each of them can move in any orientation or direction. The proposed method can generate a series of continuous and physically accurate intermediate views from only two reference images without any knowledge about 3D. The procedure consists of three steps: segmentation, morphing and post-warping. Given a boundary connection constraint, the source and target scenes are segmented into several layers for morphing. Based on the decomposition of affine transformation between corresponding points, we uniquely determine a physically correct path for post-warping by the least distortion method. I have successfully generalized the dynamic scene synthesis problem from the simple scene with only rotation to the dynamic scene containing non-rigid objects. My method can handle dynamic rigid or non-rigid objects, including complicated objects such as humans. Finally, I have also developed a novel algorithm for tri-view morphing. This is an efficient image-based method to navigate a scene based on only three wide-baseline un-calibrated images without the explicit use of a 3D model. After automatically recovering corresponding points between each pair of images using our wide baseline matching method, an accurate trifocal plane is extracted from the trifocal tensor implied in these three images. Next, employing a trinocular-stereo algorithm and barycentric blending technique, we generate an arbitrary novel view to navigate the scene in a 2D space. Furthermore, after self-calibration of the cameras, a 3D model can also be correctly augmented into this virtual environment synthesized by the tri-view morphing algorithm. We have applied our view morphing framework to several interesting applications: 4D video synthesis, automatic target recognition, multi-view morphing