3D Reconstruction from a Single Still Image Based on Monocular Vision of an Uncalibrated Camera

we propose a framework of combining Machine Learning with Dynamic Optimization for reconstructing scene in 3D automatically from a single still image of unstructured outdoor environment based on monocular vision of an uncalibrated camera. After segmenting image first time, a kind of searching tree strategy based on Bayes rule is used to identify the hierarchy of all areas on occlusion. After superpixel segmenting image second time, the AdaBoost algorithm is applied in the integration detection to the depth of lighting, texture and material. Finally, all the factors above are optimized with constrained conditions, acquiring the whole depthmap of an image. Integrate the source image with its depthmap in point-cloud or bilinear interpolation styles, realizing 3D reconstruction. Experiment in comparisons with typical methods in associated database demonstrates our method improves the reasonability of estimation to the overall 3D architecture of image’s scene to a certain extent. And it does not need any manual assist and any camera model information

3D Hallway Modeling Using a Single Image

Real-time, low-resource corridor reconstruction using a single consumer grade RGB camera is a powerful tool for allowing a fast, inexpensive solution to indoor mobility of a visually impaired person or a robot. The perspective and known geometry of a corridor is used to extract the important features of the image and create a 3D model from a single image. Multiple 3D models can be combined to increase confidence and provide a global 3D model. This paper presents our results on 3D corridor modeling using single images. First a simple but effective 3D corridor modeling approach is introduced which makes very few assumptions of the camera information. Second, a perspective based Hough transform algorithm is proposed to detect vertical lines in order to determine the edges of the corridor. Finally, issues in real-time implementation on a smartphone are discussed. Experimental results are provided to validate the proposed approach. Index Terms-- indoor modeling, vanishing point, visual impairment, perspective based Hough transform

Automatic 3D City Modeling Using a Digital Map and Panoramic Images from a Mobile Mapping System

Three-dimensional city models are becoming a valuable resource because of their close geospatial, geometrical, and visual relationship with the physical world. However, ground-oriented applications in virtual reality, 3D navigation, and civil engineering require a novel modeling approach, because the existing large-scale 3D city modeling methods do not provide rich visual information at ground level. This paper proposes a new framework for generating 3D city models that satisfy both the visual and the physical requirements for ground-oriented virtual reality applications. To ensure its usability, the framework must be cost-effective and allow for automated creation. To achieve these goals, we leverage a mobile mapping system that automatically gathers high-resolution images and supplements sensor information such as the position and direction of the captured images. To resolve problems stemming from sensor noise and occlusions, we develop a fusion technique to incorporate digital map data. This paper describes the major processes of the overall framework and the proposed techniques for each step and presents experimental results from a comparison with an existing 3D city model

On-line, Incremental Visual Scene Understanding for an Indoor Navigating Robot.

An indoor navigating robot must perceive its local environment in order to act. The robot must construct a model that captures critical navigation information from the stream of visual data that it acquires while traveling within the environment. Visual processing must be done on-line and efficiently to keep up with the robot's need. This thesis contributes both representations and algorithms toward solving the problem of modeling the local environment for an indoor navigating robot. Two representations, Planar Semantic Model (PSM) and Action Opportunity Star (AOS), are proposed to capture important navigation information of the local indoor environment. PSM models the geometric structure of the indoor environment in terms of ground plane and walls, and captures rich relationships among the wall segments. AOS is an abstracted representation that reasons about the navigation opportunities at a given pose. Both representations are capable of capturing incomplete knowledge where representations of unknown regions can be incrementally built as observations become available. An on-line generate-and-test framework is presented to construct the PSM from a stream of visual data. The framework includes two key elements, an incremental process of generating structural hypotheses and an on-line hypothesis testing mechanism using a Bayesian filter. Our framework is evaluated in three phases. First, we evaluate the effectiveness of the on-line hypothesis testing mechanism with an initially generated set of hypotheses in simple empty environments. We demonstrate that our method outperforms state-of-the-art methods on geometric reasoning both in terms of accuracy and applicability to a navigating robot. Second, we evaluate the incremental hypothesis generating process and demonstrate the expressive power of our proposed representations. At this phase, we also demonstrate an attention focusing method to efficiently discriminate among the active hypothesized models. Finally, we demonstrate a general metric to test the hypotheses with partial explanations in cluttered environments.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/108914/1/gstsai_1.pd