We present two novel parallel algorithms for rapidly constructing bounding volume hierarchies on manycore GPUs. The first uses a linear ordering derived from spatial Morton codes to build hierarchies extremely quickly and with high parallel scalability. The second is a top-down approach that uses the surface area heuristic (SAH) to build hierarchies optimized for fast ray tracing. Both algorithms are combined into a hybrid algorithm that removes existing bottlenecks in the algorithm for GPU construction performance and scalability leading to significantly decreased build time. The resulting hierarchies are close in to optimized SAH hierarchies, but the construction process is substantially faster, leading to a significant net benefit when both construction and traversal cost are accounted for. Our preliminary results show that current GPU architectures can compete with CPU implementations of hierarchy construction running on multicore systems. In practice, we can construct hierarchies of models with up to several million triangles and use them for fast ray tracing or other applications.

/pdf/fast-bvh-construction-on-gpus-1yw2gn0oje.pdf

Fast BVH Construction on GPUs

Stream compaction is a common parallel primitive used to remove unwanted elements in sparse data. This allows highly parallel algorithms to maintain performance over several processing steps and reduces overall memory usage. For wide SIMD many-core architectures, we present a novel stream compaction algorithm and explore several variations thereof. Our algorithm is designed to maximize concurrent execution, with minimal use of synchronization. Bandwidth and auxiliary storage requirements are reduced significantly, which allows for substantially better performance. We have tested our algorithms using CUDA on a PC with an NVIDIA GeForce GTX280 GPU. On this hardware, our reference implementation provides a 3× speedup over previous published algorithms. Copyright

Efficient stream compaction on wide SIMD many-core architectures

Approximate TF-IDF based on topic extraction from massive message stream using the GPU

In this paper, we propose a novel method of real-time object detection that can recognize three-dimensional (3D) target objects, regardless of their texture and lighting condition changes. Our method computes a set of reference templates of a target object from both RGB and depth images, which describes the texture and geometry of the object, and fuses them for robust detection. Combining both pieces of information has advantages over the sole use of RGB images: 1) the capability of detecting 3D objects with insufficient textures and complex shapes; 2) robust detection under varying lighting conditions; 3) better identification of a target based on its size. Our approach is inspired by a recent work on templatebased detection, and we show how to extend it with depth information, which results in better detection performance under varying lighting conditions. Intensive computations are parallelized on a GPU to achieve real-time speed, and it takes only about 33 milliseconds for detection and pose estimation. The proposed method can be used for marker-less AR applications using real-world 3D objects, beyond conventional planar target objects.

Depth-assisted Real-time 3D Object Detection for Augmented Reality

In this paper, we present a new algorithm for interactive rendering of animated scenes with Whitted Ray-Tracing, running on the GPU. We focus our attention on the secondary rays (the rays generated by one or more bounces on specular objects), and use the GPU rasterizer for primary rays. Our algorithm is based on a ray-space hierarchy, allowing us to handle truly dynamic scenes without the need to rebuild or update the scene hierarchy. The rayspace hierarchy is entirely built on the GPU for every frame, using a very fast process. Traversing the ray-space hierarchy is also done on the GPU; one of the benefits of using a ray-space hierarchy is that we have a single shader, and a fixed number of passes. After traversing each level of the hierarchy, we prune empty branches using a stream reduction method. We present two different stream reduction methods, a fast one using a hierarchical algorithm, and an easy one using the Geometry shaders. Our algorithm results in interactive rendering with specular reflections and shadows for moderately complex scenes (˜ 700K triangles), handles any kind of dynamic or unstructured scenes without any pre-processing, and scales well with both the scene complexity and the image resolution.

https://hal.inria.fr/hal-00171587/document

Whitted ray-tracing for dynamic scenes using a ray-space hierarchy on the GPU

Stream reduction is the process of removing unwanted
elements from a stream of outputs. It is a key component of many GPGPU algorithms, especially in multi-pass algorithms: the stream reduction is used to remove unwanted elements from the output of a previous pass before sending it as input for the next pass.
In this paper, we present a new efficient algorithm for stream reduction on the GPU. Our algorithm works by splitting the input stream into smaller components of a fixed size, on which we run a standard stream reduction pass. We then concatenate the results of these stream reduction pass with line drawing. This last pass is very efficient because we already know the size of the data.

/pdf/efficient-stream-reduction-on-the-gpu-57qfyld5t8.pdf

Efficient stream reduction on the GPU

Specular reflections provide many important visual cues in our daily environment They inform us of the shape of objects, of the material they are made of, of their relative positions, etc Specular reflections on curved objects are usually approximated using environment maps In this paper, we present a new algorithm for real-time computation of specular reflections on curved objects, based on an exact computation for the reflection of each scene vertex Our method exhibits all the required parallax effects and can handle arbitrary proximity between the reflector and the reflected objects

/pdf/accurate-specular-reflections-in-real-time-yukve75cm2.pdf

Accurate Specular Reflections in Real-Time

Le calcul des reﬂexions speculaires est necessaire pour atteindre un haut degre de realisme dans une image de synthese. De nombreuses methodes permettent de le faire de facon approximative en temps reel, notam- ment par l'utilisation de textures type environment map calculees a partir du reﬂecteur. Nous proposons ici une approche differente qui calcule le reﬂet de chaque sommet de la scene, puis utilise le pipeline graphique classique pour interpoler entre les sommets. La methode proposee cherche un extremum de la distance parcourue par la lumiere pour calculer le reﬂet de chaque sommet de la scene, selon le principe de Fermat. Le materiel graphique realise ensuite l'interpolation entre les reﬂets des sommets.

https://hal.inria.fr/inria-00441634/document

Réflexions spéculaires en temps réel sur des surfaces lisses

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

David Roger

Author Tools

Chat about Author

Papers

Whitted ray-tracing for dynamic scenes using a ray-space hierarchy on the GPU

Efficient stream reduction on the GPU

Accurate Specular Reflections in Real-Time

Réflexions spéculaires en temps réel sur des surfaces lisses