We present a complete system architecture for fully automated markerless augmented reality (AR). The system constructs a sparse metric model of the real-world environment, provides interactive means for specifying the pose of a virtual object, and performs model-based camera tracking with visually pleasing augmentation results. Our approach does not require camera pre-calibration, prior knowledge of scene geometry, manual initialization of the tracker or placement of special markers. Robust tracking in the presence of occlusions and scene changes is achieved by using highly distinctive natural features to establish image correspondences.

/pdf/scene-modelling-recognition-and-tracking-with-invariant-1n047b0w6c.pdf

Scene Modelling, Recognition and Tracking with Invariant Image Features

Many applications of 3D object recognition, such as augmented reality or robotic manipulation, require an accurate solution for the 3D pose of the recognized objects. This is best accomplished by building a metrically accurate 3D model of the object and all its feature locations, and then fitting this model to features detected in new images. In this chapter, we describe a system for constructing 3D metric models from multiple images taken with an uncalibrated handheld camera, recognizing these models in new images, and precisely solving for object pose. This is demonstrated in an augmented reality application where objects must be recognized, tracked, and superimposed on new images taken from arbitrary viewpoints without perceptible jitter. This approach not only provides for accurate pose, but also allows for integration of features from multiple training images into a single model that provides for more reliable recognition.

/pdf/what-and-where-3d-object-recognition-with-accurate-pose-ymmjokgqyr.pdf

What and Where: 3D Object Recognition with Accurate Pose

This paper presents a novel probabilistic foundation for volumetric 3D reconstruction. We formulate the problem as inference in a Markov random field, which accurately captures the dependencies between the occupancy and appearance of each voxel, given all input images. Our main contribution is an approximate highly parallelized discrete-continuous inference algorithm to compute the marginal distributions of each voxel's occupancy and appearance. In contrast to the MAP solution, marginals encode the underlying uncertainty and ambiguity in the reconstruction. Moreover, the proposed algorithm allows for a Bayes optimal prediction with respect to a natural reconstruction loss. We compare our method to two state-of-the-art volumetric reconstruction algorithms on three challenging aerial datasets with LIDAR ground truth. Our experiments demonstrate that the proposed algorithm compares favorably in terms of reconstruction accuracy and the ability to expose reconstruction uncertainty.

/pdf/towards-probabilistic-volumetric-reconstruction-using-ray-ivz7hdf01d.pdf

Towards Probabilistic Volumetric Reconstruction Using Ray Potentials

Structure from motion (SfM) has been an active research area in computer vision for decades and numerous practical applications are benefiting from this research. While no previous work has tried to summarize the applications appearing in the literature, this paper deals with a comprehensive overview of recent applications of SfM by classifying them into 10 categories, namely augmented reality, autonomous navigation/guidance, motion capture, hand-eye calibration, image/video processing, image-based 3D modeling, remote sensing, image organization/browsing, segmentation and recognition, and military applications. The goal is to provide insights for researchers to position their work more appropriately in the context of existing techniques, and to perceive both new applications and relevant research problems.

Applications of structure from motion: a survey

Dense height map estimation from oblique aerial image sequences

We describe initial work on a system for augmenting video sequences with 3-D graphics or animations so that they appear to be present within the scene. Our aim is to do this in real-time for sequences captured by uncalibrated ‘live’ cameras, such as a hand-held or wearable. These sequences typically contain jitter and can have narrow baselines between frames over extended time intervals. The paper focuses on obtaining accurate estimates of the 3-D motion and position of the camera for these types of sequences. We present a method based on sparse feature tracking and the recursive structure from motion algorithm developed by Azarbayejani and Pentland [1]. Our contribution is to report experiments which demonstrate that the approach performs well for sequences with jitter and narrow baselines and to discuss implementation issues relating to its use in a ‘live’ real-time system.

Robust Estimation of 3-D Camera Motion for Uncalibrated Augmented Reality

We describe a novel approach to view interpolation from image sequences based on probabilistic depth carving. This builds a multivalued representation of depth for novel views consisting of likelihoods of depth samples corresponding to either opaque or free space points. The likelihoods are obtained from iterative probabilistic combination of local disparity estimates about a subset of reference frames. This avoids the difficult problem of correspondence matching across distant views and leads to an explicit representation of occlusion. Novel views are generated by combining pixel values from the reference frames based on estimates of surface points within the likelihood representation. Efficient implementation is achieved using a multiresolution framework. Results of experiments on real image sequences show that the technique is effective.

Interpolating Novel Views from Image Sequences by Probabilistic Depth carving

Interpolating Novel Views from Image Sequences by Probabilistic Depth Carving

We describe initial work on a system for augmenting video sequences with 3-D graphics so that they appear to be present within the scene. Our aim is to do this in real time for sequences captured by uncalibrated 'live' cameras, such as a hand-held or wearable. The paper focuses on obtaining 3-D camera motion and depth estimates for these types of sequences using sparse feature tracking and the recursive algorithm developed by A. Azarbayejani and A.P. Pentland (1995). We report experiments which demonstrate that the approach performs well and discuss implementation issues relating to its use in a 'live' real-time system.

Uncalibrated Narrow Baseline Augmented Reality

Uncalibrated narrow baseline augmented reality

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