23 accepted papers
The success of data mixing augmentations in image classification tasks has been well-received. However, these techniques cannot be readily applied to object detection due to challenges such as spatial misalignment, foreground/background distinction, and plurality of instances. To tackle these issues…
We present the design of a productionized end-to-end stereo depth sensing system that does pre-processing, online stereo rectification, and stereo depth estimation with a fallback to monocular depth estimation when rectification is unreliable. The output of our depth sensing system is then used in a…
We propose a fast and generalizable solution to Multiview Photometric Stereo (MVPS), called MVPSNet. The key to our approach is a feature extraction network that effectively combines images from the same view captured under multiple lighting conditions to extract geometric features from shading cues…
We propose a boundary-aware multi-task deep-learning-based framework for fast 3D building modeling from a single overhead image. Unlike most existing techniques which rely on multiple images for 3D scene modeling, we seek to model the buildings in the scene from a single overhead image by jointly le…
In this work, we propose "tangent images," a spherical image representation that facilitates transferable and scalable 360 degree computer vision. Inspired by techniques in cartography and computer graphics, we render a spherical image to a set of distortion-mitigated, locally-planar image grids tan…
We present a novel single-view surface normal estimation method that combines traditional line and vanishing point analysis with a deep learning approach. Starting from a color image and a Manhattan line map, we use a deep neural network to regress on a dense normal map, and a dense Manhattan label…
Deep learning-based, single-view depth estimation methods have recently shown highly promising results. However, such methods ignore one of the most important features for determining depth in the human vision system, which is motion. We propose a learning-based, multi-view dense depth map and odome…
We present a novel framework for edge-aware optimization that is an order of magnitude faster than the state of the art while maintaining comparable results. Our key insight is that the optimization can be formulated by leveraging properties of the domain transform, a method for edge-aware filtering…
Image-based 3D reconstruction for Internet photo collections has become a robust technology to produce impressive virtual representations of real-world scenes. However, several fundamental challenges remain for Structure-from-Motion (SfM) pipelines, namely: the placement and reconstruction of transi…
We introduce a method for improving convolutional neural networks (CNNs) for scene classification. We present a hierarchy of specialist networks, which disentangles the intra-class variation and inter-class similarity in a coarse to fine manner. Our key insight is that each subset within a class is…
Traditionally, camera-based tracking approaches have treated rolling shutter and radial distortion as imaging artifacts that have to be overcome and corrected for in order to apply standard camera models and scene reconstruction methods. In this paper, we introduce a novel multi-camera tracking appr…
We address the problem of large scale image geo-localization where the location of an image is estimated by identifying geo-tagged reference images depicting the same place. We propose a novel model for learning image representations that integrates context-aware feature reweighting in order to effe…
There is a significant interest in scene reconstruction from underwater images given its utility for oceanic research and for recreational image manipulation. In this paper we propose a novel algorithm for two view camera motion estimation for underwater imagery. Our method leverages the constraints…
Internet photo collections naturally contain a large variety of illumination conditions, with the largest difference between day and night images. Current modeling techniques do not embrace the broad illumination range often leading to reconstruction failure or severe artifacts. We present an algori…
Incremental Structure-from-Motion is a prevalent strategy for 3D reconstruction from unordered image collections. While incremental reconstruction systems have tremendously advanced in all regards, robustness, accuracy, completeness, and scalability remain the key problems towards building a truly g…
We present a novel, continuous, locally optimal calibration scheme for use with head-worn devices. Current calibration schemes solve for a globally optimal model of the eye-device transformation by performing calibration on a per-user or once-per-use basis. However, these calibration schemes are i…
Structure-from-Motion for unordered image collections has significantly advanced in scale over the last decade. This impressive progress can be in part attributed to the introduction of efficient retrieval methods for those systems. While this boosts scalability, it also limits the amount of detail…
We propose two novel minimal solvers which advance the state of the art in satellite imagery processing. Our methods are efficient and do not rely on the prior existence of complex inverse mapping functions to correlate 2D image coordinates and 3D terrain. Our first solver improves on the stereo cor…
Large-scale Structure-from-Motion systems typically spend major computational effort on pairwise image matching and geometric verification in order to discover connected components in large-scale, unordered image collections. In recent years, the research community has spent significant effort on im…
We address the problem of recognizing a place depicted in a query image by using a large database of geo-tagged images at a city-scale. In particular, we discover features that are useful for recognizing a place in a data-driven manner, and use this knowledge to predict useful features in a query im…
We propose a novel, large-scale, structure-from-motion framework that advances the state of the art in data scalability from city-scale modeling (millions of images) to world-scale modeling (several tens of millions of images) using just a single computer. The main enabling technology is the use of…
We target the sparse 3D reconstruction of dynamic objects observed by multiple unsynchronized video cameras with unknown temporal overlap. To this end, we develop a framework to recover the unknown structure without sequencing information across video sequences. Our proposed compressed sensing frame…
We propose a framework for the automatic creation of time-lapse mosaics of a given scene. We achieve this by leveraging the illumination variations captured in Internet photo-collections. In order to depict and characterize the illumination spectrum of a scene, our method relies on building discrete…