GASP : Geometric Association with Surface Patches

A fundamental challenge to sensory processing tasks in perception and robotics is the problem of obtaining data associations across views. We present a robust solution for ascertaining potentially dense surface patch (superpixel) associations, requiring just range information. Our approach involves decomposition of a view into regularized surface patches. We represent them as sequences expressing geometry invariantly over their superpixel neighborhoods, as uniquely consistent partial orderings. We match these representations through an optimal sequence comparison metric based on the Damerau-Levenshtein distance - enabling robust association with quadratic complexity (in contrast to hitherto employed joint matching formulations which are NP-complete). The approach is able to perform under wide baselines, heavy rotations, partial overlaps, significant occlusions and sensor noise. The technique does not require any priors -- motion or otherwise, and does not make restrictive assumptions on scene structure and sensor movement. It does not require appearance -- is hence more widely applicable than appearance reliant methods, and invulnerable to related ambiguities such as textureless or aliased content. We present promising qualitative and quantitative results under diverse settings, along with comparatives with popular approaches based on range as well as RGB-D data.Comment: International Conference on 3D Vision, 201

From Multiview Image Curves to 3D Drawings

Reconstructing 3D scenes from multiple views has made impressive strides in recent years, chiefly by correlating isolated feature points, intensity patterns, or curvilinear structures. In the general setting - without controlled acquisition, abundant texture, curves and surfaces following specific models or limiting scene complexity - most methods produce unorganized point clouds, meshes, or voxel representations, with some exceptions producing unorganized clouds of 3D curve fragments. Ideally, many applications require structured representations of curves, surfaces and their spatial relationships. This paper presents a step in this direction by formulating an approach that combines 2D image curves into a collection of 3D curves, with topological connectivity between them represented as a 3D graph. This results in a 3D drawing, which is complementary to surface representations in the same sense as a 3D scaffold complements a tent taut over it. We evaluate our results against truth on synthetic and real datasets.Comment: Expanded ECCV 2016 version with tweaked figures and including an overview of the supplementary material available at multiview-3d-drawing.sourceforge.ne

Doppelgangers: Learning to Disambiguate Images of Similar Structures

We consider the visual disambiguation task of determining whether a pair of visually similar images depict the same or distinct 3D surfaces (e.g., the same or opposite sides of a symmetric building). Illusory image matches, where two images observe distinct but visually similar 3D surfaces, can be challenging for humans to differentiate, and can also lead 3D reconstruction algorithms to produce erroneous results. We propose a learning-based approach to visual disambiguation, formulating it as a binary classification task on image pairs. To that end, we introduce a new dataset for this problem, Doppelgangers, which includes image pairs of similar structures with ground truth labels. We also design a network architecture that takes the spatial distribution of local keypoints and matches as input, allowing for better reasoning about both local and global cues. Our evaluation shows that our method can distinguish illusory matches in difficult cases, and can be integrated into SfM pipelines to produce correct, disambiguated 3D reconstructions. See our project page for our code, datasets, and more results: http://doppelgangers-3d.github.io/.Comment: Published in ICCV 2023 (Oral); Project page: http://doppelgangers-3d.github.io

Toward Efficient and Robust Large-Scale Structure-from-Motion Systems

The ever-increasing number of images that are uploaded and shared on the Internet has recently been leveraged by computer vision researchers to extract 3D information about the content seen in these images. One key mechanism to extract this information is structure-from-motion, which is the process of recovering the 3D geometry (structure) of a scene via a set of images from different viewpoints (camera motion). However, when dealing with crowdsourced datasets comprised of tens or hundreds of millions of images, the magnitude and diversity of the imagery poses challenges such as robustness, scalability, completeness, and correctness for existing structure-from-motion systems. This dissertation focuses on these challenges and demonstrates practical methods to address the problems of data association and verification within structure-from-motion systems. Data association within structure-from-motion systems consists of the discovery of pairwise image overlap within the input dataset. In order to perform this discovery, previous systems assumed that information about every image in the input dataset could be stored in memory, which is prohibitive for large-scale photo collections. To address this issue, we propose a novel streaming-based framework for the discovery of related sets of images, and demonstrate our approach on a crowdsourced dataset containing 100 million images from all around the world. Results illustrate that our streaming-based approach does not compromise model completeness, but achieves unprecedented levels of efficiency and scalability. The verification of individual data associations is difficult to perform during the process of structure-from-motion, as standard methods have limited scope when determining image overlap. Therefore, it is possible for erroneous associations to form, especially when there are symmetric, repetitive, or duplicate structures which can be incorrectly associated with each other. The consequences of these errors are incorrectly placed cameras and scene geometry within the 3D reconstruction. We present two methods that can detect these local inconsistencies and successfully resolve them into a globally consistent 3D model. In our evaluation, we show that our techniques are efficient, are robust to a variety of scenes, and outperform existing approaches.Doctor of Philosoph

The Iray Light Transport Simulation and Rendering System

While ray tracing has become increasingly common and path tracing is well understood by now, a major challenge lies in crafting an easy-to-use and efficient system implementing these technologies. Following a purely physically-based paradigm while still allowing for artistic workflows, the Iray light transport simulation and rendering system allows for rendering complex scenes by the push of a button and thus makes accurate light transport simulation widely available. In this document we discuss the challenges and implementation choices that follow from our primary design decisions, demonstrating that such a rendering system can be made a practical, scalable, and efficient real-world application that has been adopted by various companies across many fields and is in use by many industry professionals today