Registration and Recognition in 3D

The simplest Computer Vision algorithm can tell you what color it sees when you point it at an object, but asking that computer what it is looking at is a much harder problem. Camera and LiDAR (Light Detection And Ranging) sensors generally provide streams pixel of values and sophisticated algorithms must be engineered to recognize objects or the environment. There has been significant effort expended by the computer vision community on recognizing objects in color images; however, LiDAR sensors, which sense depth values for pixels instead of color, have been studied less. Recently we have seen a renewed interest in depth data with the democratization provided by consumer depth cameras. Detecting objects in depth data is more challenging in some ways because of the lack of texture and increased complexity of processing unordered point sets. We present three systems that contribute to solving the object recognition problem from the LiDAR perspective. They are: calibration, registration, and object recognition systems. We propose a novel calibration system that works with both line and raster based LiDAR sensors, and calibrates them with respect to image cameras. Our system can be extended to calibrate LiDAR sensors that do not give intensity information. We demonstrate a novel system that produces registrations between different LiDAR scans by transforming the input point cloud into a Constellation Extended Gaussian Image (CEGI) and then uses this CEGI to estimate the rotational alignment of the scans independently. Finally we present a method for object recognition which uses local (Spin Images) and global (CEGI) information to recognize cars in a large urban dataset. We present real world results from these three systems. Compelling experiments show that object recognition systems can gain much information using only 3D geometry. There are many object recognition and navigation algorithms that work on images; the work we propose in this thesis is more complimentary to those image based methods than competitive. This is an important step along the way to more intelligent robots

Localization of Autonomous Vehicles in Urban Environments

The future of applications such as last-mile delivery, infrastructure inspection and surveillance bets big on employing small autonomous drones and ground robots in cluttered urban settings where precise positioning is critical. However, when navigating close to buildings, GPS-based localisation of robotic platforms is noisy due to obscured reception and multi-path reflection. Localisation methods using introspective sensors like monocular and stereo cameras mounted on the platforms offer a better alternative as they are suitable for both indoor and outdoor operations. However, the inherent drift in the estimated trajectory is often evident in the 7 degrees of freedom that captures scaling, rotation and translation motion, and needs to be corrected. The theme of the thesis is to use a pre-existing 3D model to supplement the pose estimation from a visual navigation system, reducing incremental drift and thereby improving localisation accuracy. The novel framework developed for the monocular camera first extracts the geometric relationship between the pixels of the calibrated camera and the 3D points on the model. These geometric constraints, when used in addition to the relative pose constraints typically used in Simultaneous Localisation and Mapping (SLAM) algorithms, provide superior trajectory estimation. Further, scale drift correction is proposed using a novel $SIM_3$ optimisation procedure and successfully demonstrated using a unique dataset that embodies many urban localisation challenges. Techniques developed for Stereo camera localisation aligns the textured 3D stereo scans with respect to a 3D model and estimates the associated camera pose. The idea is to solve the image registration problem between the projection of the 3D scan and images whose poses are accurately known with respect to the 3D model. The 2D motion parameters are then mapped to the 3D space for camera pose estimation. Novel image registration techniques are developed which use image edge information combined with traditional approaches to show successful results

Automatic Reconstruction of Textured 3D Models

Three dimensional modeling and visualization of environments is an increasingly important problem. This work addresses the problem of automatic 3D reconstruction and we present a system for unsupervised reconstruction of textured 3D models in the context of modeling indoor environments. We present solutions to all aspects of the modeling process and an integrated system for the automatic creation of large scale 3D models

Determination of the Transverse Horizontal Axis and Interocclusal Registration Using a Novel Optical Technique

Motivation: Dental treatments sometimes require the recording and reproduction of the patient’s transverse horizontal axis (THA). This is the axis about which the mandible will rotate, when the condyles are fully seated in their glenoid fossae. However, methods for recording this axis are rarely used in general practice, due to expense and perceived lack of efficacy. Problem Statement: A simplified method for accurately recording the THA, and interocclusal records (IORs), is needed for general dentistry. Approach: An optical 3D scanning method is proposed to kinematically record the THA. A simulation determines the required hardware specifications to build the scanner at minimal cost. The ability to record the hinge axis of a dental articulator is explored. High quality interocclusal optical records are essential, and these are investigated in subsequent experiments. Areas for improvements are identified and efforts are made to enhance the system speed and calibration. Results: Simulation results indicated that all 6 upper and lower anterior teeth, including 2mm of gingivae should be captured, with an accuracy of 50µm. The THA on a dental articulator could be located with a radial accuracy of 2.65±1.01mm. The repeatability (precision) of IORs showed a standard deviation of 22µm anteriorly, and a mean of 43µm posteriorly in vitro. The accuracy (trueness and precision) of the IORs was -15±22µm anteriorly, and up to -93±121µm posteriorly in vitro. A faster scanning protocol enabled in vivo testing. 29 IORs of a single subject took <2s to perform. The registrations showed a repeatability of 31µm anteriorly and 70µm posteriorly. A novel calibration process produced significantly reduced stereo reprojection errors compared to traditional methods (0.22 vs 0.27 pixels), offering a potential future system enhancement. Conclusions: The proposed method shows potential to improve the speed, accuracy and simplicity with which the THA, and interocclusal registration, can be recorded. Further developments have been suggested prior to embarking on clinical trials

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

大規模観測対象のための幾何および光学情報の統合

University of Tokyo (東京大学