Classification of Vehicles using Monocular 3D Reconstruction

State of the art 3D reconstruction techniques utilize frames from a video sequence to render a 3D model of the scene. Our 3D reconstruction technique utilizes Speeded-Up Robust Features along with optical flow points to create a dense point cloud. Each point within the model has been tracked from frame to frame and triangulated into its (X,Y,Z) model position. We present an application for these structure from motion models that exploits our previous work in 3D object classification. In our experiments, we reconstruct a parking lot scene that contains several vehicles. The first step of our object classification algorithm is to segment each of the vehicles. Then, for each separate point cluster, our algorithm utilizes the volumetric and shape properties of the 3D object to label it with a vehicle type. The novelty of this classification approach allows us to tackle the noise challenges commonly associated with monocular 3D reconstructed models.https://ecommons.udayton.edu/stander_posters/1652/thumbnail.jp

Analysis of 3D Face Reconstruction

This thesis investigates the long standing problem of 3D reconstruction from a single 2D face image. Face reconstruction from a single 2D face image is an ill posed problem involving estimation of the intrinsic and the extrinsic camera parameters, light parameters, shape parameters and the texture parameters. The proposed approach has many potential applications in the law enforcement, surveillance, medicine, computer games and the entertainment industries. This problem is addressed using an analysis by synthesis framework by reconstructing a 3D face model from identity photographs. The identity photographs are a widely used medium for face identi cation and can be found on identity cards and passports. The novel contribution of this thesis is a new technique for creating 3D face models from a single 2D face image. The proposed method uses the improved dense 3D correspondence obtained using rigid and non-rigid registration techniques. The existing reconstruction methods use the optical ow method for establishing 3D correspondence. The resulting 3D face database is used to create a statistical shape model. The existing reconstruction algorithms recover shape by optimizing over all the parameters simultaneously. The proposed algorithm simplifies the reconstruction problem by using a step wise approach thus reducing the dimension of the parameter space and simplifying the opti- mization problem. In the alignment step, a generic 3D face is aligned with the given 2D face image by using anatomical landmarks. The texture is then warped onto the 3D model by using the spatial alignment obtained previously. The 3D shape is then recovered by optimizing over the shape parameters while matching a texture mapped model to the target image. There are a number of advantages of this approach. Firstly, it simpli es the optimization requirements and makes the optimization more robust. Second, there is no need to accurately recover the illumination parameters. Thirdly, there is no need for recovering the texture parameters by using a texture synthesis approach. Fourthly, quantitative analysis is used for improving the quality of reconstruction by improving the cost function. Previous methods use qualitative methods such as visual analysis, and face recognition rates for evaluating reconstruction accuracy. The improvement in the performance of the cost function occurs as a result of improvement in the feature space comprising the landmark and intensity features. Previously, the feature space has not been evaluated with respect to reconstruction accuracy thus leading to inaccurate assumptions about its behaviour. The proposed approach simpli es the reconstruction problem by using only identity images, rather than placing eff ort on overcoming the pose, illumination and expression (PIE) variations. This makes sense, as frontal face images under standard illumination conditions are widely available and could be utilized for accurate reconstruction. The reconstructed 3D models with texture can then be used for overcoming the PIE variations

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

3D Face Reconstruction from Light Field Images: A Model-free Approach

Reconstructing 3D facial geometry from a single RGB image has recently instigated wide research interest. However, it is still an ill-posed problem and most methods rely on prior models hence undermining the accuracy of the recovered 3D faces. In this paper, we exploit the Epipolar Plane Images (EPI) obtained from light field cameras and learn CNN models that recover horizontal and vertical 3D facial curves from the respective horizontal and vertical EPIs. Our 3D face reconstruction network (FaceLFnet) comprises a densely connected architecture to learn accurate 3D facial curves from low resolution EPIs. To train the proposed FaceLFnets from scratch, we synthesize photo-realistic light field images from 3D facial scans. The curve by curve 3D face estimation approach allows the networks to learn from only 14K images of 80 identities, which still comprises over 11 Million EPIs/curves. The estimated facial curves are merged into a single pointcloud to which a surface is fitted to get the final 3D face. Our method is model-free, requires only a few training samples to learn FaceLFnet and can reconstruct 3D faces with high accuracy from single light field images under varying poses, expressions and lighting conditions. Comparison on the BU-3DFE and BU-4DFE datasets show that our method reduces reconstruction errors by over 20% compared to recent state of the art

TVL₁ Planarity Regularization for 3D Shape Approximation

The modern emergence of automation in many industries has given impetus to extensive research into mobile robotics. Novel perception technologies now enable cars to drive autonomously, tractors to till a field automatically and underwater robots to construct pipelines. An essential requirement to facilitate both perception and autonomous navigation is the analysis of the 3D environment using sensors like laser scanners or stereo cameras. 3D sensors generate a very large number of 3D data points when sampling object shapes within an environment, but crucially do not provide any intrinsic information about the environment which the robots operate within. This work focuses on the fundamental task of 3D shape reconstruction and modelling from 3D point clouds. The novelty lies in the representation of surfaces by algebraic functions having limited support, which enables the extraction of smooth consistent implicit shapes from noisy samples with a heterogeneous density. The minimization of total variation of second differential degree makes it possible to enforce planar surfaces which often occur in man-made environments. Applying the new technique means that less accurate, low-cost 3D sensors can be employed without sacrificing the 3D shape reconstruction accuracy