Building with Drones: Accurate 3D Facade Reconstruction using MAVs

Automatic reconstruction of 3D models from images using multi-view Structure-from-Motion methods has been one of the most fruitful outcomes of computer vision. These advances combined with the growing popularity of Micro Aerial Vehicles as an autonomous imaging platform, have made 3D vision tools ubiquitous for large number of Architecture, Engineering and Construction applications among audiences, mostly unskilled in computer vision. However, to obtain high-resolution and accurate reconstructions from a large-scale object using SfM, there are many critical constraints on the quality of image data, which often become sources of inaccuracy as the current 3D reconstruction pipelines do not facilitate the users to determine the fidelity of input data during the image acquisition. In this paper, we present and advocate a closed-loop interactive approach that performs incremental reconstruction in real-time and gives users an online feedback about the quality parameters like Ground Sampling Distance (GSD), image redundancy, etc on a surface mesh. We also propose a novel multi-scale camera network design to prevent scene drift caused by incremental map building, and release the first multi-scale image sequence dataset as a benchmark. Further, we evaluate our system on real outdoor scenes, and show that our interactive pipeline combined with a multi-scale camera network approach provides compelling accuracy in multi-view reconstruction tasks when compared against the state-of-the-art methods.Comment: 8 Pages, 2015 IEEE International Conference on Robotics and Automation (ICRA '15), Seattle, WA, US

Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery

One of the main challenges for computer-assisted surgery (CAS) is to determine the intra-opera- tive morphology and motion of soft-tissues. This information is prerequisite to the registration of multi-modal patient-specific data for enhancing the surgeon’s navigation capabilites by observ- ing beyond exposed tissue surfaces and for providing intelligent control of robotic-assisted in- struments. In minimally invasive surgery (MIS), optical techniques are an increasingly attractive approach for in vivo 3D reconstruction of the soft-tissue surface geometry. This paper reviews the state-of-the-art methods for optical intra-operative 3D reconstruction in laparoscopic surgery and discusses the technical challenges and future perspectives towards clinical translation. With the recent paradigm shift of surgical practice towards MIS and new developments in 3D opti- cal imaging, this is a timely discussion about technologies that could facilitate complex CAS procedures in dynamic and deformable anatomical regions

Real Time Sequential Non Rigid Structure from motion using a single camera

En la actualidad las aplicaciones que basan su funcionamiento en una correcta localización y reconstrucción dentro de un entorno real en 3D han experimentado un gran interés en los últimos años, tanto por la comunidad investigadora como por la industrial. Estas aplicaciones varían desde la realidad aumentada, la robótica, la simulación, los videojuegos, etc. Dependiendo de la aplicación y del nivel de detalle de la reconstrucción, se emplean diversos dispositivos, algunos específicos, más complejos y caros como las cámaras estéreo, cámara y profundidad (RGBD) con Luz estructurada y Time of Flight (ToF), así como láser y otros más avanzados. Para aplicaciones sencillas es suficiente con dispositivos de uso común, como los smartphones, en los que aplicando técnicas de visión artificial, se pueden obtener modelos 3D del entorno para, en el caso de la realidad aumentada, mostrar información aumentada en la ubicación seleccionada.En robótica, la localización y generación simultáneas de un mapa del entorno en 3D es una tarea fundamental para conseguir la navegación autónoma. Este problema se conoce en el estado del arte como Simultaneous Localization And Mapping (SLAM) o Structure from Motion (SfM). Para la aplicación de estas técnicas, el objeto no ha de cambiar su forma a lo largo del tiempo. La reconstrucción es unívoca salvo factor de escala en captura monocular sin referencia. Si la condición de rigidez no se cumple, es porque la forma del objeto cambia a lo largo del tiempo. El problema sería equivalente a realizar una reconstrucción por fotograma, lo cual no se puede hacer de manera directa, puesto que diferentes formas, combinadas con diferentes poses de cámara pueden dar proyecciones similares. Es por esto que el campo de la reconstrucción de objetos deformables es todavía un área en desarrollo. Los métodos de SfM se han adaptado aplicando modelos físicos, restricciones temporales, espaciales, geométricas o de otros tipos para reducir la ambigüedad en las soluciones, naciendo así las técnicas conocidas como Non-Rigid SfM (NRSfM).En esta tesis se propone partir de una técnica de reconstrucción rígida bien conocida en el estado del arte como es PTAM (Parallel Tracking and Mapping) y adaptarla para incluir técnicas de NRSfM, basadas en modelo de bases lineales para estimar las deformaciones del objeto modelado dinámicamente y aplicar restricciones temporales y espaciales para mejorar las reconstrucciones, además de ir adaptándose a cambios de deformación que se presenten en la secuencia. Para ello, hay que realizar cambios de manera que cada uno de sus hilos de ejecución procesen datos no rígidos.El hilo encargado del seguimiento ya realizaba seguimiento basado en un mapa de puntos 3D, proporcionado a priori. La modificación más importante aquí es la integración de un modelo de deformación lineal para que se realice el cálculo de la deformación del objeto en tiempo real, asumiendo fijas las formas básicas de deformación. El cálculo de la pose de la cámara está basado en el sistema de estimación rígido, por lo que la estimación de pose y coeficientes de deformación se hace de manera alternada usando el algoritmo E-M (Expectation-Maximization). También, se imponen restricciones temporales y de forma para restringir las ambigüedades inherentes en las soluciones y mejorar la calidad de la estimación 3D.Respecto al hilo que gestiona el mapa, se actualiza en función del tiempo para que sea capaz de mejorar las bases de deformación cuando éstas no son capaces de explicar las formas que se ven en las imágenes actuales. Para ello, se sustituye la optimización de modelo rígido incluida en este hilo por un método de procesamiento exhaustivo NRSfM, para mejorar las bases acorde a las imágenes con gran error de reconstrucción desde el hilo de seguimiento. Con esto, el modelo se consigue adaptar a nuevas deformaciones, permitiendo al sistema evolucionar y ser estable a largo plazo.A diferencia de una gran parte de los métodos de la literatura, el sistema propuesto aborda el problema de la proyección perspectiva de forma nativa, minimizando los problemas de ambigüedad y de distancia al objeto existente en la proyección ortográfica. El sistema propuesto maneja centenares de puntos y está preparado para cumplir con restricciones de tiempo real para su aplicación en sistemas con recursos hardware limitados

Windowed Factorization and Merging

In this work, an online 3D reconstruction algorithm is proposed which attempts to solve the structure from motion problem for occluded and degenerate data. To deal with occlusion the temporal consistency of data within a limited window is used to compute local reconstructions. These local reconstructions are transformed and merged to obtain an estimation of the 3D object shape. The algorithm is shown to accurately reconstruct a rotating and translating artificial sphere and a rotating toy dinosaur from a video. The proposed algorithm (WIFAME) provides a versatile framework to deal with missing data in the structure from motion problem

A comparative study of breast surface reconstruction for aesthetic outcome assessment

Breast cancer is the most prevalent cancer type in women, and while its survival rate is generally high the aesthetic outcome is an increasingly important factor when evaluating different treatment alternatives. 3D scanning and reconstruction techniques offer a flexible tool for building detailed and accurate 3D breast models that can be used both pre-operatively for surgical planning and post-operatively for aesthetic evaluation. This paper aims at comparing the accuracy of low-cost 3D scanning technologies with the significantly more expensive state-of-the-art 3D commercial scanners in the context of breast 3D reconstruction. We present results from 28 synthetic and clinical RGBD sequences, including 12 unique patients and an anthropomorphic phantom demonstrating the applicability of low-cost RGBD sensors to real clinical cases. Body deformation and homogeneous skin texture pose challenges to the studied reconstruction systems. Although these should be addressed appropriately if higher model quality is warranted, we observe that low-cost sensors are able to obtain valuable reconstructions comparable to the state-of-the-art within an error margin of 3 mm.Comment: This paper has been accepted to MICCAI201

A Comparison of Monocular Visual SLAM and Visual Odometry Methods Applied to 3D Reconstruction

This work was supported by the SDAS Research Group (www.sdas-group.com accessed on 16 June 2023).Pure monocular 3D reconstruction is a complex problem that has attracted the research community's interest due to the affordability and availability of RGB sensors. SLAM, VO, and SFM are disciplines formulated to solve the 3D reconstruction problem and estimate the camera's ego-motion; so, many methods have been proposed. However, most of these methods have not been evaluated on large datasets and under various motion patterns, have not been tested under the same metrics, and most of them have not been evaluated following a taxonomy, making their comparison and selection difficult. In this research, we performed a comparison of ten publicly available SLAM and VO methods following a taxonomy, including one method for each category of the primary taxonomy, three machine-learning-based methods, and two updates of the best methods to identify the advantages and limitations of each category of the taxonomy and test whether the addition of machine learning or updates on those methods improved them significantly. Thus, we evaluated each algorithm using the TUM-Mono dataset and benchmark, and we performed an inferential statistical analysis to identify the significant differences through its metrics. The results determined that the sparse-direct methods significantly outperformed the rest of the taxonomy, and fusing them with machine learning techniques significantly enhanced the geometric-based methods' performance from different perspectives.SDAS Research Grou

3D Human Face Reconstruction and 2D Appearance Synthesis

3D human face reconstruction has been an extensive research for decades due to its wide applications, such as animation, recognition and 3D-driven appearance synthesis. Although commodity depth sensors are widely available in recent years, image based face reconstruction are significantly valuable as images are much easier to access and store. In this dissertation, we first propose three image-based face reconstruction approaches according to different assumption of inputs. In the first approach, face geometry is extracted from multiple key frames of a video sequence with different head poses. The camera should be calibrated under this assumption. As the first approach is limited to videos, we propose the second approach then focus on single image. This approach also improves the geometry by adding fine grains using shading cue. We proposed a novel albedo estimation and linear optimization algorithm in this approach. In the third approach, we further loose the constraint of the input image to arbitrary in the wild images. Our proposed approach can robustly reconstruct high quality model even with extreme expressions and large poses. We then explore the applicability of our face reconstructions on four interesting applications: video face beautification, generating personalized facial blendshape from image sequences, face video stylizing and video face replacement. We demonstrate great potentials of our reconstruction approaches on these real-world applications. In particular, with the recent surge of interests in VR/AR, it is increasingly common to see people wearing head-mounted displays. However, the large occlusion on face is a big obstacle for people to communicate in a face-to-face manner. Our another application is that we explore hardware/software solutions for synthesizing the face image with presence of HMDs. We design two setups (experimental and mobile) which integrate two near IR cameras and one color camera to solve this problem. With our algorithm and prototype, we can achieve photo-realistic results. We further propose a deep neutral network to solve the HMD removal problem considering it as a face inpainting problem. This approach doesn\u27t need special hardware and run in real-time with satisfying results