Registration of Point Cloud Data (PCD) forms a core component of many 3D vision algorithms such as object matching and environment reconstruction. In this paper, we
introduce a PCD registration algorithm that utilizes Gaussian Mixture Models (GMM) and a novel dual-mode parameter optimization technique which we call mixture decoupling.

We introduce a method for accurate three dimensional head pose estimation using a commodity depth camera. We perform pose estimation by registering a morphable face model to the measured depth data, using a combination of particle swarm optimization (PSO) and the iterative closest point (ICP) algorithm, which minimizes a cost function that includes a 3D registration and a 2D overlap term. The pose is estimated on the fly without requiring an explicit initialization or training phase.

Accumulative anti-aliasing (ACAA) is a simple modification of forward-rendered multi-sample anti-aliasing (MSAA). It produces the same image quality but consumes half as much multi-sample framebuffer memory, and reduces both render time and off-chip bandwidth by 20% to 30%. ACAA stores multiple depth samples, computed by a depth-only pre-pass, but stores only one color sample per pixel, which is used to accumulate final color as the sum of shaded fragment colors weighted by visibility. ACAA makes higher sample rates practical, improving image quality.

As deep nets are increasingly used in applications suited for mobile devices, a fundamental dilemma becomes apparent: the trend in deep learning is to grow models to absorb ever-increasing data set sizes; however mobile devices are designed with very little memory and cannot store such large models. We present a novel network architecture, HashedNets, that exploits inherent redundancy in neural networks to achieve drastic reductions in model sizes.

The human visual system can operate in a wide range of illumination levels, due to several adaptation processes working in concert. For the most part, these adaptation mechanisms are transparent, leaving the observer unaware of his or her absolute adaptation state. At extreme illumination levels, however, some of these mechanisms produce perceivable secondary effects, or epiphenomena. In bright light, these include bleaching afterimages and adaptation afterimages, while in dark conditions these include desaturation, loss of acuity, mesopic hue shift, and the Purkinje effect.

Image registration for stack-based HDR photography is challenging. If not properly accounted for, camera motion and scene changes result in artifacts in the composite image.

Unfortunately, existing methods to address this problem are either accurate, but too slow for mobile devices, or fast, but prone to failing. We propose a method that fills this void: our approach is extremely fast—under 700ms on a commercial tablet for a pair of 5MP images-and prevents the artifacts that arise from insufficient registration quality.

We report on the use of a CMOS Contrast-based Binary Vision Sensor (CBVS), with embedded contrast extraction, for gesture detection applications.
The first advantage of using this sensor over commercial imagers is a dynamic range of 120dB, made possible by a pixel design that effectively performs auto-exposure control.

We introduce apex point map, a simple data structure for constructing conservative bounds for rigid objects. The data structure is distilled from a dense k-DOP, and can be queried in constant time to determine a tight bounding plane with any given normal vector. Both precalculation and lookup can be implemented very efficiently on current GPUs. Applications include, e.g., finding tight world-space bounds for transformed meshes, determining per-object shadow map extents, more accurate view frustum culling, and collision detection.

We present a method for extreme occluder simplification. We take a triangle soup as input, and produce a small set of polygons with closely matching occlusion properties. In contrast to methods that optimize the original geometry, our algorithm has very few requirements for the input—specifically, the input does not need to be a watertight, two-manifold mesh. This robustness is achieved by working on a well-behaved, discretized representation of the input instead of the original, potentially badly structured geometry.

We propose an efficient acceleration structure for real-time screen-space ray tracing. The hybrid data structure represents the scene geometry by combining a bounding volume hierarchy with local planar approximations. This enables fast empty space skipping while tracing and yields exact intersection points for the planar approximation. In combination with an occlusion-aware ray traversal our algorithm is capable to quickly trace even multiple depth layers.