Optical neural network via loose neuron array and functional learning.

This research proposes a deep-learning paradigm, termed functional learning (FL), to physically train a loose neuron array, a group of non-handcrafted, non-differentiable, and loosely connected physical neurons whose connections and gradients are beyond explicit expression. The paradigm targets training non-differentiable hardware, and therefore solves many interdisciplinary challenges at once: the precise modeling and control of high-dimensional systems, the on-site calibration of multimodal hardware imperfectness, and the end-to-end training of non-differentiable and modeless physical neurons through implicit gradient propagation. It offers a methodology to build hardware without handcrafted design, strict fabrication, and precise assembling, thus forging paths for hardware design, chip manufacturing, physical neuron training, and system control.

Instant Panoramic Texture Mapping with Semantic Object Matching for Large-Scale Urban Scene Reproduction.

This paper proposes a novel panoramic texture mapping-based rendering system for real-time, photorealistic reproduction of large-scale urban scenes at a street level. Various image-based rendering (IBR) methods have recently been employed to synthesize high-quality novel views, although they require an excessive number of adjacent input images or detailed geometry just to render local views. While the development of global data, such as Google Street View, has accelerated interactive IBR techniques for urban scenes, such methods have hardly been aimed at high-quality street-level rendering.

Physically-inspired Deep Light Estimation from a Homogeneous-Material Object for Mixed Reality Lighting.

In mixed reality (MR), augmenting virtual objects consistently with real-world illumination is one of the key factors that provide a realistic and immersive user experience. For this purpose, we propose a novel deep learning-based method to estimate high dynamic range (HDR) illumination from a single RGB image of a reference object. To obtain illumination of a current scene, previous approaches inserted a special camera in that scene, which may interfere with user's immersion, or they analyzed reflected radiances from a passive light probe with a specific type of materials or a known shape.

Distance Encoded Product Quantization for Approximate K-Nearest Neighbor Search in High-Dimensional Space.

Approximate K-nearest neighbor search is a fundamental problem in computer science. The problem is especially important for high-dimensional and large-scale data. Recently, many techniques encoding high-dimensional data to compact codes have been proposed.

Spherical hashing: binary code embedding with hyperspheres.

Many binary code embedding schemes have been actively studied recently, since they can provide efficient similarity search, and compact data representations suitable for handling large scale image databases. Existing binary code embedding techniques encode high-dimensional data by using hyperplane-based hashing functions. In this paper we propose a novel hypersphere-based hashing function, spherical hashing, to map more spatially coherent data points into a binary code compared to hyperplane-based hashing functions.

T-ReX: interactive global illumination of massive models on heterogeneous computing resources.

We propose several interactive global illumination techniques for a diverse set of massive models. We integrate these techniques within a progressive rendering framework that aims to achieve both a high rendering throughput and an interactive responsiveness. To achieve a high rendering throughput, we utilize heterogeneous computing resources consisting of CPU and GPU.

Scheduling in heterogeneous computing environments for proximity queries.

We present a novel, linear programming (LP)-based scheduling algorithm that exploits heterogeneous multicore architectures such as CPUs and GPUs to accelerate a wide variety of proximity queries. To represent complicated performance relationships between heterogeneous architectures and different computations of proximity queries, we propose a simple, yet accurate model that measures the expected running time of these computations. Based on this model, we formulate an optimization problem that minimizes the largest time spent on computing resources, and propose a novel, iterative LP-based scheduling algorithm.

RACBVHs: random-accessible compressed bounding volume hierarchies.

We present a novel compressed bounding volume hierarchy (BVH) representation, random-accessible compressed bounding volume hierarchies (RACBVHs), for various applications requiring random access on BVHs of massive models. Our RACBVH representation is compact and transparently supports random access on the compressed BVHs without decompressing the whole BVH. To support random access on our compressed BVHs, we decompose a BVH into a set of clusters.

ICCD: interactive continuous collision detection between deformable models using connectivity-based culling.

We present an interactive algorithm for continuous collision detection between deformable models. We introduce multiple techniques to improve the culling efficiency and the overall performance of continuous collision detection. First, we present a novel formulation for continuous normal cones and use these normal cones to efficiently cull large regions of the mesh as part of self-collision tests.

Massive-model rendering techniques.

The currently observed exponentially increasing size of 3D models prohibits rendering them using brute force methods. Researchers have proposed various output-sensitive rendering algorithms to overcome this challenge. This article provides an overview of this technology.

Random-accessible compressed triangle meshes.

With the exponential growth in size of geometric data, it is becoming increasingly important to make effective use of multilevel caches, limited disk storage, and bandwidth. As a result, recent work in the visualization community has focused either on designing sequential access compression schemes or on producing cache-coherent layouts of (uncompressed) meshes for random access. Unfortunately combining these two strategies is challenging as they fundamentally assume conflicting modes of data access.

Mesh layouts for block-based caches.

Current computer architectures employ caching to improve the performance of a wide variety of applications. One of the main characteristics of such cache schemes is the use of block fetching whenever an uncached data element is accessed. To maximize the benefit of the block fetching mechanism, we present novel cache-aware and cache-oblivious layouts of surface and volume meshes that improve the performance of interactive visualization and geometric processing algorithms.

Quick-VDR: out-of-core view-dependent rendering of gigantic models.

We present a novel approach for interactive view-dependent rendering of massive models. Our algorithm combines view-dependent simplification, occlusion culling, and out-of-core rendering. We represent the model as a clustered hierarchy of progressive meshes (CHPM).