High-efficient view planning for surface inspection based on parallel deep reinforcement learning

Efficient similarity search on multidimensional space of biometric databases

Although regarded as a standard heuristic for constructing a kd-tree, Surface Area Heuristic (SAH) suffers from degrading traversal performance as well as unnecessary acceleration structure construction due to its assumption of uniform ray distribution. In this paper, first, we propose a Grid based Ray Distribution Heuristic (GRDH) to construct a high quality kd-tree. The information of ray distribution is sampled and recorded in a sparse grid, which is also utilized to partition primitives efficiently. The heuristic only evaluating traversal cost on limited splitting candidates, is fast and easy to be implemented. Then, we introduce a novel ray tracing pipeline with two-pass construction and two-pass tracing routine to enable an on-demand construction algorithm. In the pipeline, after constructing a partially structured kd-tree, rays are classified by whether triggering transitions to construct unstructured leaf nodes, and being traced in different tracing passes. For on-demand construction, this raises the barrier of interleaving with traversal routine, as well as enables the algorithm to be flexible on multi-core computation platform. Additionally, GRDH is also combined into on-demand construction for high traversal performance. The experimental results demonstrate that the on-demand GRDH construction algorithm can achieve a speedup of 1.63ź~ź1.95 in overall frame performance for scenes with more than 360K primitives.

Efficient kd-tree construction for ray tracing using ray distribution sampling

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.

Jun Yin

Author Tools

Chat about Author

Papers

Efficient kd-tree construction for ray tracing using ray distribution sampling