Practical SVBRDF Acquisition of 3D Objects with Unstructured Flash Photography

Capturing spatially-varying bidirectional reflectance distribution functions (SVBRDFs) of 3D objects with just a single, hand-held camera (such as an off-the-shelf smartphone or a DSLR camera) is a difficult, open problem. Previous works are either limited to planar geometry, or rely on previously scanned 3D geometry, thus limiting their practicality. There are several technical challenges that need to be overcome: First, the built-in flash of a camera is almost colocated with the lens, and at a fixed position; this severely hampers sampling procedures in the light-view space. Moreover, the near-field flash lights the object partially and unevenly. In terms of geometry, existing multiview stereo techniques assume diffuse reflectance only, which leads to overly smoothed 3D reconstructions, as we show in this paper. We present a simple yet powerful framework that removes the need for expensive, dedicated hardware, enabling practical acquisition of SVBRDF information from real-world, 3D objects with a single, off-the-shelf camera with a built-in flash. In addition, by removing the diffuse reflection assumption and leveraging instead such SVBRDF information, our method outputs high-quality 3D geometry reconstructions, including more accurate high-frequency details than state-of-the-art multiview stereo techniques. We formulate the joint reconstruction of SVBRDFs, shading normals, and 3D geometry as a multi-stage, iterative inverse-rendering reconstruction pipeline. Our method is also directly applicable to any existing multiview 3D reconstruction technique. We present results of captured objects with complex geometry and reflectance; we also validate our method numerically against other existing approaches that rely on dedicated hardware, additional sources of information, or both

WiSlow: A WiFi Network Performance Troubleshooting Tool for End Users

The increasing number of 802.11 APs and wireless devices results in more contention, which causes unsatisfactory WiFi network performance. In addition, non-WiFi devices sharing the same spectrum with 802.11 networks such as microwave ovens, cordless phones, and baby monitors severely interfere with WiFi networks. Although the problem sources can be easily removed in many cases, it is difficult for end users to identify the root cause. We introduce WiSlow, a software tool that diagnoses the root causes of poor WiFi performance with user-level network probes and leverages peer collaboration to identify the location of the causes. We elaborate on two main methods: packet loss analysis and 802.11 ACK pattern analysis

Towards A Dynamic QoS-aware Over-The-Top Video Streaming in LTE

We present a study of traffic behavior of two popular over-the-top (OTT) video streaming services (YouTube and Netflix). Our analysis is conducted on different mobile devices (iOS and Android) over various wireless networks (Wi-Fi, 3G and LTE) under dynamic network conditions. Our measurements show that the video players frequently discard a large amount of video content although it is successfully delivered to a client. We first investigate the root cause of this unwanted behavior. Then, we propose a Quality-of-Service (QoS)-aware video streaming architecture in Long Term Evolution (LTE) networks to reduce the waste of network resource and improve user experience. The architecture includes a selective packet discarding mechanism, which can be placed in packet data network gateways (P-GW). In addition, our QoS-aware rules assist video players in selecting an appropriate resolution under a fluctuating channel condition. We monitor network condition and configure QoS parameters to control availability of the maximum bandwidth in real time. In our experimental setup, the proposed platform shows up to 20.58% improvement in saving downlink bandwidth and improves user experience by reducing buffer underflow period to an average of 32 seconds

High-quality hyperspectral reconstruction using a spectral prior

We present a novel hyperspectral image reconstruction algorithm, which overcomes the long-standing tradeoff between spectral accuracy and spatial resolution in existing compressive imaging approaches. Our method consists of two steps: First, we learn nonlinear spectral representations from real-world hyperspectral datasets; for this, we build a convolutional autoencoder, which allows reconstructing its own input through its encoder and decoder networks. Second, we introduce a novel optimization method, which jointly regularizes the fidelity of the learned nonlinear spectral representations and the sparsity of gradients in the spatial domain, by means of our new fidelity prior. Our technique can be applied to any existing compressive imaging architecture, and has been thoroughly tested both in simulation, and by building a prototype hyperspectral imaging system. It outperforms the state-of-the-art methods from each architecture, both in terms of spectral accuracy and spatial resolution, while its computational complexity is reduced by two orders of magnitude with respect to sparse coding techniques. Moreover, we present two additional applications of our method: hyperspectral interpolation and demosaicing. Last, we have created a new high-resolution hyperspectral dataset containing sharper images of more spectral variety than existing ones, available through our project website

Towards Dynamic Network Condition-Aware Video Server Selection Algorithms over Wireless Networks

We investigate video server selection algorithms in a distributed video-on-demand system. We conduct a detailed study of the YouTube Content Delivery Network (CDN) on PCs and mobile devices over Wi-Fi and 3G networks under varying network conditions. We proved that a location-aware video server selection algorithm assigns a video content server based on the network attachment point of a client. We found out that such distance-based algorithms carry the risk of directing a client to a less optimal content server, although there may exist other better performing video delivery servers. In order to solve this problem, we propose to use dynamic network information such as packet loss rates and Round Trip Time (RTT)between an edge node of an wireless network (e.g., an Internet Service Provider (ISP) router in a Wi-Fi network and a Radio Network Controller (RNC) node in a 3G network) and video content servers, to find the optimal video content server when a video is requested. Our empirical study shows that the proposed architecture can provide higher TCP performance, leading to better viewing quality compared to location-based video server selection algorithms

Mobile Video Is Inefficient: A Traffic Analysis

Video streaming on mobile devices is on the rise. According to recent reports, mobile video streaming traffic accounted for 52.8% of total mobile data traffic in 2011, and it is forecast to reach 66.4% in 2015. We analyzed the network traffic behaviors of the two most popular HTTP-based video streaming services: YouTube and Netflix. Our research indicates that the network traffic behavior depends on factors such as the type of device, multimedia applications in use and network conditions. Furthermore, we found that a large part of the downloaded video content can be unaccepted by a video player even though it is successfully delivered to a client. This unwanted behavior often occurs when the video player changes the resolution in a fluctuating network condition and the playout buffer is full while downloading a video. Some of the measurements show that the discarded data may exceed 35% of the total video content

Socioeconomic Status and Early Savings Outcomes: Evidence From a Statewide Child Development Account Experiment

Socioeconomic Status and Early Savings Outcomes: Evidence From a Statewide Child Development Account Experimen

Are Child Development Accounts Inclusive? Early Evidence From a Statewide Experiment

A key objective of Child Development Accounts (CDAs) is to increase college completion rates among disadvantaged youth by helping families accumulate assets for college and by encouraging youth to see themselves as college bound. While the major asset-building programs in the United States largely benefit socioeconomically advantaged individuals, CDAs explicitly aim to facilitate account holding and asset accumulation by disadvantaged families. But do CDAs meet the goal of being inclusive? This research uses data from a large CDA experiment with probability sampling and random assignment to examine early CDA savings outcomes. Findings indicate that the CDA improves outcomes for several demographic groups and has a greater impact on some disadvantaged groups than on their advantaged counterparts. Features like automatic account opening and automatic initial deposits, which are uncommon in other asset-building programs, extend the opportunities and benefits of asset accumulation to disadvantaged families