Error Detection, Factorization and Correction for Multi-View Scene Reconstruction from Aerial Imagery

Scene reconstruction from video sequences has become a prominent computer vision research area in recent years, due to its large number of applications in fields such as security, robotics and virtual reality. Despite recent progress in this field, there are still a number of issues that manifest as incomplete, incorrect or computationally-expensive reconstructions. The engine behind achieving reconstruction is the matching of features between images, where common conditions such as occlusions, lighting changes and texture-less regions can all affect matching accuracy. Subsequent processes that rely on matching accuracy, such as camera parameter estimation, structure computation and non-linear parameter optimization, are also vulnerable to additional sources of error, such as degeneracies and mathematical instability. Detection and correction of errors, along with robustness in parameter solvers, are a must in order to achieve a very accurate final scene reconstruction. However, error detection is in general difficult due to the lack of ground-truth information about the given scene, such as the absolute position of scene points or GPS/IMU coordinates for the camera(s) viewing the scene. In this dissertation, methods are presented for the detection, factorization and correction of error sources present in all stages of a scene reconstruction pipeline from video, in the absence of ground-truth knowledge. Two main applications are discussed. The first set of algorithms derive total structural error measurements after an initial scene structure computation and factorize errors into those related to the underlying feature matching process and those related to camera parameter estimation. A brute-force local correction of inaccurate feature matches is presented, as well as an improved conditioning scheme for non-linear parameter optimization which applies weights on input parameters in proportion to estimated camera parameter errors. Another application is in reconstruction pre-processing, where an algorithm detects and discards frames that would lead to inaccurate feature matching, camera pose estimation degeneracies or mathematical instability in structure computation based on a residual error comparison between two different match motion models. The presented algorithms were designed for aerial video but have been proven to work across different scene types and camera motions, and for both real and synthetic scenes

Optical Flow in Mostly Rigid Scenes

The optical flow of natural scenes is a combination of the motion of the observer and the independent motion of objects. Existing algorithms typically focus on either recovering motion and structure under the assumption of a purely static world or optical flow for general unconstrained scenes. We combine these approaches in an optical flow algorithm that estimates an explicit segmentation of moving objects from appearance and physical constraints. In static regions we take advantage of strong constraints to jointly estimate the camera motion and the 3D structure of the scene over multiple frames. This allows us to also regularize the structure instead of the motion. Our formulation uses a Plane+Parallax framework, which works even under small baselines, and reduces the motion estimation to a one-dimensional search problem, resulting in more accurate estimation. In moving regions the flow is treated as unconstrained, and computed with an existing optical flow method. The resulting Mostly-Rigid Flow (MR-Flow) method achieves state-of-the-art results on both the MPI-Sintel and KITTI-2015 benchmarks.Comment: 15 pages, 10 figures; accepted for publication at CVPR 201

Non-Rigid Structure from Motion for Complex Motion

Recovering deformable 3D motion from temporal 2D point tracks in a monocular video is an open problem with many everyday applications throughout science and industry, or the new augmented reality. Recently, several techniques have been proposed to deal the problem called Non-Rigid Structure from Motion (NRSfM), however, they can exhibit poor reconstruction performance on complex motion. In this project, we will analyze these situations for primitive human actions such as walk, run, sit, jump, etc. on different scenarios, reviewing first the current techniques to finally present our novel method. This approach is able to model complex motion into a union of subspaces, rather than the summation occurring in standard low-rank shape methods, allowing better reconstruction accuracy. Experiments in a wide range of sequences and types of motion illustrate the benefits of this new approac

{3D} Morphable Face Models -- Past, Present and Future

In this paper, we provide a detailed survey of 3D Morphable Face Models over the 20 years since they were first proposed. The challenges in building and applying these models, namely capture, modeling, image formation, and image analysis, are still active research topics, and we review the state-of-the-art in each of these areas. We also look ahead, identifying unsolved challenges, proposing directions for future research and highlighting the broad range of current and future applications

Combining Dense Nonrigid Structure from Motion and 3D Morphable Models for Monocular 4D Face Reconstruction

This is the author accepted manuscript. The final version is available from ACM via the DOI in this record Monocular 4D face reconstruction is a challenging problem, especially in the case that the input video is captured under unconstrained conditions, i.e. "in the wild". The majority of the state-of-the-art approaches build upon 3D Morphable Modelling (3DMM), which has been proven to be more robust than model-free approaches such as Shape from Shading (SfS) or Structure from Motion (SfM). While offering visually plausible shape reconstruction results that resemble real faces, 3DMMs adhere to the model space learned from exemplar faces during the training phase, often yielding facial reconstructions that are excessively smooth and look too similar even across captured faces with completely different facial characteristics. This is due to the fact that 3DMMs are typically used as hard constraints on the reconstructed 3D shape. To overcome these limitations, in this paper we propose to combine 3DMMs with Dense Nonrigid Structure from Motion (DNSM), which is much less robust but has the potential of reconstructing fine details and capturing the subject-specific facial characteristics of every input. We effectively combine the best of both worlds by introducing a novel dense variational framework, which we solve efficiently by designing a convex optimisation strategy. In contrast to previous methods, we incorporate 3DMM as a soft constraint, penalizing both departure of reconstructed faces from the 3DMM subspace and variation of the identity component of the 3DMM over different frames of the input video. As demonstrated in qualitative and quantitative experiments, our method is robust, accurately estimates the 3D facial shape over time and outperforms other state-of-the-art methods of 4D face reconstruction

Object modeling using a ToF camera under an uncertainty reduction approach

Trabajo presentado al ICRA 2010 celebrado en Anchorage (Alaska) del3 al 7 de mayo.Time-of-Flight (ToF) cameras deliver 3D images at 25 fps, offering great potential for developing fast object modeling algorithms. Surprisingly, this potential has not been extensively exploited up to now. A reason for this is that, since the acquired depth images are noisy, most of the available registration algorithms are hardly applicable. A further difficulty is that the transformations between views are in general not accurately known, a circumstance that multi-view object modeling algorithms do not handle properly under noisy conditions. In this work, we take into account both uncertainty sources (in images and camera poses) to generate spatially consistent 3D object models fusing multiple views with a probabilistic approach. We propose a method to compute the covariance of the registration process, and apply an iterative state estimation method to build object models under noisy conditions.This work was supported by projects: 'Perception, action & cognition through learning of object-action complexes.' (4915), 'CONSOL IDER-INGENIO 2010 Multimodal interaction in pattern recognition and computer vision' (V-00069), 'Percepción y acción ante incertidumbre' (4803). This work has been partially supported by the Spanish Ministry of Science and Innovation under project DPI2008-06022, the MIPRCV Consolider Ingenio 2010 project, and the EU PACO PLUS project FP6-2004-IST-4-27657. S. Foix and G. Alenyà are supported by PhD and postdoctoral fellowships, respectively, from CSIC’s JAE program.Peer Reviewe

Quantitative 3d reconstruction from scanning electron microscope images based on affine camera models

Scanning electron microscopes (SEMs) are versatile imaging devices for the micro-and nanoscale that find application in various disciplines such as the characterization of biological, mineral or mechanical specimen. Even though the specimen’s two-dimensional (2D) properties are provided by the acquired images, detailed morphological characterizations require knowledge about the three-dimensional (3D) surface structure. To overcome this limitation, a reconstruction routine is presented that allows the quantitative depth reconstruction from SEM image sequences. Based on the SEM’s imaging properties that can be well described by an affine camera, the proposed algorithms rely on the use of affine epipolar geometry, self-calibration via factorization and triangulation from dense correspondences. To yield the highest robustness and accuracy, different sub-models of the affine camera are applied to the SEM images and the obtained results are directly compared to confocal laser scanning microscope (CLSM) measurements to identify the ideal parametrization and underlying algorithms. To solve the rectification problem for stereo-pair images of an affine camera so that dense matching algorithms can be applied, existing approaches are adapted and extended to further enhance the yielded results. The evaluations of this study allow to specify the applicability of the affine camera models to SEM images and what accuracies can be expected for reconstruction routines based on self-calibration and dense matching algorithms. © MDPI AG. All rights reserved