Abstract

A light field is a 4D function which captures all the radiance information of a scene. Image-based creation of light fields reconstructs the 4D space using pre-captured imagery from various views and employs refocusing to generate output images. Handheld cameras can also capture light fields using a microlens array between the sensor and the main lens, but physical constraints limit their spatial and angular resolutions. In this paper, we present a GPU based synthetic light field rendering framework that is robust and physically accurate. We demonstrate the equivalence of the standard light field camera representation with light slab representation for synthetic light fields and exhibit the capability of our framework to trace light fields of resolutions much higher than available in commercial plenoptic cameras. The light slab is rich in quality but bulky to store. Our system provides parameters to balance the quality and storage requirements. We also present a compact representation of the 4D light slabs using a video compression codec and demonstrate different quality-size combinations using these representations.

Abstract

Focus, depth-of-field, and defocus are important ele-ments that portray the aesthetic emphasis in a good pho-tograph. The ability to manipulate the focus after capture provides useful creative control to photographers. Captur-ing focal stacks - multiple images with small change in focus setting - of static scenes is relatively easy with modern cam-eras. We propose a compact representation for focal stacks using an all-in-focus image, a focal-slice index map and pair-wise defocus blur parameters. Using our representation, we show reconstruction of images with different focus effects including extended focus, multiple focus, and scene synthesis with natural focus effects. A user study shows high acceptability of the synthesized images compared to real ones. The compact and powerful representation of focal stacks makes them suitable for handling by image editing tools in order to provide flexible focus manipulation

Abstract

Depth from focus is a highly accessible method to estimate the 3D structure of everyday scenes. Today’s DSLR and mobile cameras facilitate the easy capture of multiple focused images of a scene. Focus measures (FMs) that estimate the amount of focus at each pixel form the basis of depth-from-focus methods. Several FMs have been proposed in the past and new ones will emerge in the future, each with their own strengths. We estimate a weighted combination of standard FMs that outperforms others on a wide range of scene types. The resulting composite focus measure consists of FMs that are in consensus with one another but not in chorus. Our two-stage pipeline first estimates fine depth at each pixel using the composite focus measure. A cost-volume propagation step then assigns depths from confident pixels to others. We can generate high quality depth maps using just the top five FMs from our composite focus measure. This is a positive step towards depth estimation of everyday scenes with no special equipment.

Abstract

Smartphones have become the de-facto capture devices for everyday photography. Unlike traditional digital cameras, smartphones are versatile devices with auxiliary sensors, processing power, and networking capabilities. In this work, we harness the communication capabilities of smartphones and present a synchronous/co-ordinated multi-camera capture system. Synchronous capture is important for many image/video fusion and 3D reconstruction applications. The proposed system provides an inexpensive and effective means to capture multi-camera media for such applications. Our coordinated capture system is based on a wireless protocol that uses NTP based synchronization and device specific lag compensation. It achieves sub-frame synchronization across all participating smartphones of even heterogeneous make and model. We propose a new method based on fiducial markers displayed on an LCD screen to temporally calibrate smartphone cameras. We demonstrate the utility and versatility of this system to enhance traditional videography and to create novel visual representations such as panoramic videos, HDR videos, multi-view 3D reconstruction, multi-flash imaging, and multi-camera social media.

Abstract

View-graph is an essential input to large-scale structure from motion (SfM) pipelines. Accuracy and efficiency of large-scale SfM is crucially dependent on the input viewgraph. Inconsistent or inaccurate edges can lead to inferior or wrong reconstruction. Most SfM methods remove ‘undesirable’ images and pairs using several, fixed heuristic criteria, and propose tailor-made solutions to achieve specific reconstruction objectives such as efficiency, accuracy, or disambiguation. In contrast to these disparate solutions, we propose a single optimization framework that can be used to achieve these different reconstruction objectives with task-specific cost modeling. We also construct a very efficient network-flow based formulation for its approximate solution. The abstraction brought on by this selection mechanism separates the challenges specific to datasets and reconstruction objectives from the standard SfM pipeline and improves its generalization. This paper demonstrates the application of the proposed view-graph framework with standard SfM pipeline for two particular use-cases, (i) accurate and ghost-free reconstructions of highly ambiguous datasets using costs based on disambiguation priors, and (ii) accurate and efficient reconstruction of large-scale Internet datasets using costs based on commonly used priors.

Abstract

Several methods have been proposed for large-scale 3D reconstruction from large, unorganized image collections. A large reconstruction problem is typically divided into multiple components which are reconstructed independently using structure from motion (SFM) and later merged together. Incremental SFM methods are most popular for the basic structure recovery of a single component. They are robust and effective but strictly sequential in nature. We present a multistage approach for SFM reconstruction of a single component that breaks the sequential nature of the incremental SFM methods. Our approach begins with quickly building a coarse 3D model using only a fraction of features from given images. The coarse model is then enriched by localizing remaining images and matching and triangulating remaining features in subsequent stages. The geometric information available in form of the coarse model allows us to make these stages effective, efficient, and highly parallel. We show that our method produces similar quality models as compared to standard SFM methods while being notably fast and parallel.

Abstract

Short internet video clips like vines present a significantly wild distribution compared to traditional video datasets. In this paper, we focus on the problem of unsupervised action classification in wild vines using traditional labeled datasets. To this end, we use a data augmentation based simple domain adaptation strategy. We utilize semantic word2vec space as a common subspace to embed video features from both, labeled source domain and unlabled target domain. Our method incrementally augments the labeled source with target samples and iteratively modifies the embedding function to bring the source and target distributions together. Additionally, we utilize a multi-modal representation that incorporates noisy semantic information available in form of hash-tags. We show the effectiveness of this simple adaptation technique on a test set of vines and achieve notable improvements in performance.

Abstract

User-given tags or labels are valuable resources for semantic understanding of visual media such as images and videos. Recently, a new type of labeling mechanism known as hashtags have become increasingly popular on social media sites. In this paper, we study the problem of generating relevant and useful hash-tags for short video clips. Traditional data driven approaches for tag enrichment and recommendation use direct visual similarity for label transfer and propagation. We attempt to learn a direct low-cost mapping from video to hash-tags using a two step training process. We first employ a natural language processing (NLP) technique, skip-gram models with neural network training to learn a low-dimensional vector representation of hash-tags (Tag2Vec) using a corpus of ∼ 10 million hash-tags. We then train an embedding function to map video features to the low-dimensional Tag2vec space. We learn this embedding for 29 categories of short video clips with hash-tags. A query video without any tag-information can then be directly mapped to the vector space of tags using the learned embedding and relevant tags can be found by performing a simple nearest-neighbor retrieval in the Tag2Vec space. We validate the relevance of the tags suggested by our system qualitatively and quantitatively with a user study.

Abstract

In this paper, we presents a novel method (RGBF-IID) for intrinsic image decomposition of a wild scene without any restrictions on the complexity, illumination or scale of the image. We use focal stacks of the scene as input. A focal stack captures a scene at varying focal distances. Since focus depends on distance to the object, this representation has information beyond an RGB image towards an RGBD image with depth. We call our representation an RGBF image to highlight this. We use a robust focus measure and generalized random walk algorithm to compute dense probability maps across the stack. These maps are used to define sparse local and global pixel neighbourhoods, adhering to the structure of the underlying 3D scene. We use these neighbourhood correspondences with standard chromaticity assumptions as constraints in an optimization system. We present our results on both indoor and outdoor scenes using manually captured stacks of random objects under natural as well as artificial lighting conditions. We also test our system on a larger dataset of synthetically generated focal stacks from NYUv2 and MPI Sintel datasets and show competitive performance against current state-of-the-art IID methods that use RGBD images. Our method provides a strong evidence for the potential of RGBF modality in place of RGBD in computer vision.

Abstract

here has been a profound interest of late in the digitization and reconstruction of historical monuments. Rendering massive monument models requires a cluster of work stations because of the computational in feasibility of rendering over a single machine. Moreover, interactive rendering of these massive models in an immersive environment is only possible over a cluster of machines. In this paper, we present a design of distributed rendering system to efficiently handle models with massive textures. A server holds the skeleton of the whole model and divides the screen space balancing the rendering load among multiple clients. Each client loads only the require geometry and textures to render its sub-scene. We present a virtual texturing method for handling massive textures over the distributed rendering system. These textures are combined into a texture atlas which is split into equally sized tiles. A virtual texture is built over this atlas with each pixel representing a tile in the atlas. An efficient caching module loads only the required tiles into the memory, that are identified using the virtual texture.A fragment shader uses the virtual texture as a mapping to the physical texture in memory to generate the fragments. We demonstrate the performance of our system over a 350M triangles and 500 gigapixel textured model of Vittala temple.