유니티 그래픽 최적화, 어디까지 해봤니 (Optimizing Unity Graphics) NDC15 Ver.

May 21, 201573 likes19,295 views

The document discusses various graphics optimization techniques for game development using Unity, including reducing draw calls through batching, profiling to identify bottlenecks, using level of detail for 3D models, image-based lighting, and physically based rendering. It provides examples of optimizing a game by changing from deferred to forward rendering, using GPU skinning, reducing polygon counts, and comparing different lighting approaches. The goal of optimization is to remove bottlenecks by properly analyzing performance using tools like the Unity profiler across different target devices and settings.

그래픽 최적화
어디까지 해봤니
- Unity Field Engineer 오지현

Bottleneck
Draw Call
Batching (Pros, Cons)
Profiling
Profiling Sample
Level Of Detail
Lighting (Image Based Lighting)
Physically Based Rendering

CPU
GPU
Frame
End
Frame
start
Bottleneck?

CPU boundary VS GPU boundary
CPU
GPU
Frame
End
Frame
start

Logic AI Physics
Loading GC ...
CPU boundary

GPU boundary
Polygon Texture Filtering
Lighting Framebuffer
Shader Overdraw ...

CPU
DRAW!
Draw Call in a narrow sense
GPU
OK

CPU
DRAW!
TEXTURE!
SHADER!
VERTEX BUFFER!
ALPHA BLENDING!
TRANSFORM
CHANGE STATES
Draw Call in a broad sense
GPU...
USE Z-BUFFER!

CPU (App)
GPU
COMMAND COMMAND COMMAND COMMAND COMMAND
Set Texture Set Shader
Use Blending
Set VBO
Draw
Command Buffer

+Draw Call
Logic AI Physics
Loading GC …
CPU boundary

Batch(in Rendering Statistics Window)
= Draw Call
+ Setting Shader + Setting Blending
+ Setting Texture + Setting Vertex Buffer
+ ...

Material A Material B
Mesh 1 Mesh 2 Mesh 1 Mesh 3
Batch Batch
s
c
e
n
e

Dynamic Batching do this on Run-time
GPU
Vertex BufferScene

Dynamic Batching: limitations
Overhead for collecting vertices.
- Not for Skinned Mesh.
- Less than 300 vertices per mesh.
(position, normal, uv0)

유니티 그래픽 최적화, 어디까지 해봤니 (Optimizing Unity Graphics) NDC15 Ver.

Analyze your Bottleneck
Measure correctly
Average value
Diverse situation
Per Target Device

CPU : A5
GPU : PowerVR SGX535
Resolution : 1024 x 768
CPU : A5X
GPU : PowerVR SGX543
Resolution : 2048 x 1536
Performance ?
Episode of

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Ensuring robust, reliable, and repeatable delivery processes is more critical than ever - it's a success factor for your automations and for automation programmes as a whole. In this session, we’ll dive into modern best practices for release management and explore how tools like the UiPathCLI can streamline your CI/CD pipelines. Whether you’re just starting with automation or scaling enterprise-grade deployments, our event promises to deliver helpful insights to you. This topic is relevant for both on-premise and cloud users - as well as for automation developers and software testers alike. 📕 Agenda: - Best Practices for Release Management - What it is and why it matters - UiPath Build Pipelines Deep Dive - Exploring CI/CD workflows, the UiPathCLI and showcasing scenarios for both on-premise and cloud - Discussion, Q&A 👨‍🏫 Speakers Roman Tobler, CEO@ Routinuum Johans Brink, CTO@ MvR Digital Workforce We look forward to bringing best practices and showcasing build pipelines to you - and to having interesting discussions on this important topic! If you have any questions or inputs prior to the event, don't hesitate to reach out to us. This event streamed live on May 27, 16:00 pm CET. Check out all our upcoming UiPath Community sessions at: 👉 https://community.uipath.com/events/ Join UiPath Community Zurich chapter: 👉 https://community.uipath.com/zurich/

Agentic AI Explained: The Next Frontier of Autonomous Intelligence & Generati...Aaryan Kansari

Agentic AI Explained: The Next Frontier of Autonomous Intelligence & Generative AI Discover Agentic AI, the revolutionary step beyond reactive generative AI. Learn how these autonomous systems can reason, plan, execute, and adapt to achieve human-defined goals, acting as digital co-workers. Explore its promise, key frameworks like LangChain and AutoGen, and the challenges in designing reliable and safe AI agents for future workflows. Sticky Note Bullets: Definition: Next stage beyond ChatGPT-like systems, offering true autonomy. Core Function: Can "reason, plan, execute and adapt" independently. Distinction: Proactive (sets own actions for goals) vs. Reactive (responds to prompts). Promise: Acts as "digital co-workers," handling grunt work like research, drafting, bug fixing. Industry Outlook: Seen as a game-changer; Deloitte predicts 50% of companies using GenAI will have agentic AI pilots by 2027. Key Frameworks: LangChain, Microsoft's AutoGen, LangGraph, CrewAI. Development Focus: Learning to think in workflows and goals, not just model outputs. Challenges: Ensuring reliability, safety; agents can still hallucinate or go astray. Best Practices: Start small, iterate, add memory, keep humans in the loop for final decisions. Use Cases: Limited only by imagination (e.g., drafting business plans, complex simulations).

Microsoft Build 2025 takeaways in one presentationDigitalmara

Microsoft Build 2025 introduced significant updates. Everything revolves around AI. DigitalMara analyzed these announcements: • AI enhancements for Windows 11 By embedding AI capabilities directly into the OS, Microsoft is lowering the barrier for users to benefit from intelligent automation without requiring third-party tools. It's a practical step toward improving user experience, such as streamlining workflows and enhancing productivity. However, attention should be paid to data privacy, user control, and transparency of AI behavior. The implementation policy should be clear and ethical. • GitHub Copilot coding agent The introduction of coding agents is a meaningful step in everyday AI assistance. However, it still brings challenges. Some people compare agents with junior developers. They noted that while the agent can handle certain tasks, it often requires supervision and can introduce new issues. This innovation holds both potential and limitations. Balancing automation with human oversight is crucial to ensure quality and reliability. • Introduction of Natural Language Web NLWeb is a significant step toward a more natural and intuitive web experience. It can help users access content more easily and reduce reliance on traditional navigation. The open-source foundation provides developers with the flexibility to implement AI-driven interactions without rebuilding their existing platforms. NLWeb is a promising level of web interaction that complements, rather than replaces, well-designed UI. • Introduction of Model Context Protocol MCP provides a standardized method for connecting AI models with diverse tools and data sources. This approach simplifies the development of AI-driven applications, enhancing efficiency and scalability. Its open-source nature encourages broader adoption and collaboration within the developer community. Nevertheless, MCP can face challenges in compatibility across vendors and security in context sharing. Clear guidelines are crucial. • Windows Subsystem for Linux is open-sourced It's a positive step toward greater transparency and collaboration in the developer ecosystem. The community can now contribute to its evolution, helping identify issues and expand functionality faster. However, open-source software in a core system also introduces concerns around security, code quality management, and long-term maintenance. Microsoft’s continued involvement will be key to ensuring WSL remains stable and secure. • Azure AI Foundry platform hosts Grok 3 AI models Adding new models is a valuable expansion of AI development resources available at Azure. This provides developers with more flexibility in choosing language models that suit a range of application sizes and needs. Hosting on Azure makes access and integration easier when using Microsoft infrastructure.

Improving Developer Productivity With DORA, SPACE, and DevExJustin Reock

Gihbli AI and Geo sitution |use/misuse of Ai Technologyzainkhurram1111

Multistream in SIP and NoSIP @ OpenSIPS Summit 2025Lorenzo Miniero

Slides for my "Multistream support in the Janus SIP and NoSIP plugins" presentation at the OpenSIPS Summit 2025 event. They describe my efforts refactoring the Janus SIP and NoSIP plugins to allow for the gatewaying of an arbitrary number of audio/video streams per call (thus breaking the current 1-audio/1-video limitation), plus some additional considerations on what this could mean when dealing with application protocols negotiated via SIP as well.

AI Emotional Actors: “When Machines Learn to Feel and Perform"AkashKumar809858

STKI Israel Market Study 2025 final v1 versionDr. Jimmy Schwarzkopf

Let’s Get Slack Certified! 🚀- Slack CommunitySanjeetMishra29

Jira Administration Training – Day 1 : IntroductionRavi Teja

Nix(OS) for Python Developers - PyCon 25 (Bologna, Italia)Peter Bittner

How do you onboard new colleagues in 2025? How long does it take? Would you love a standardized setup under version control that everyone can customize for themselves? A stable desktop setup, reinstalled in just minutes. It can be done. This talk was given in Italian, 29 May 2025, at PyCon 25, Bologna, Italy. All slides are provided in English. Original slides at https://slides.com/bittner/pycon25-nixos-for-python-developers

End-to-end Assurance for SD-WAN & SASE with ThousandEyesThousandEyes

Grannie’s Journey to Using Healthcare AI ExperiencesLauren Parr

LSNIF: Locally-Subdivided Neural Intersection FunctionTakahiro Harada

Neural representations have shown the potential to accelerate ray casting in a conventional ray-tracing-based rendering pipeline. We introduce a novel approach called Locally-Subdivided Neural Intersection Function (LSNIF) that replaces bottom-level BVHs used as traditional geometric representations with a neural network. Our method introduces a sparse hash grid encoding scheme incorporating geometry voxelization, a scene-agnostic training data collection, and a tailored loss function. It enables the network to output not only visibility but also hit-point information and material indices. LSNIF can be trained offline for a single object, allowing us to use LSNIF as a replacement for its corresponding BVH. With these designs, the network can handle hit-point queries from any arbitrary viewpoint, supporting all types of rays in the rendering pipeline. We demonstrate that LSNIF can render a variety of scenes, including real-world scenes designed for other path tracers, while achieving a memory footprint reduction of up to 106.2x compared to a compressed BVH. https://arxiv.org/abs/2504.21627

Offshore IT Support: Balancing In-House and Offshore Help Desk Techniciansjohn823664

In today's always-on digital environment, businesses must deliver seamless IT support across time zones, devices, and departments. This SlideShare explores how companies can strategically combine in-house expertise with offshore talent to build a high-performing, cost-efficient help desk operation. From the benefits and challenges of offshore support to practical models for integrating global teams, this presentation offers insights, real-world examples, and key metrics for success. Whether you're scaling a startup or optimizing enterprise support, discover how to balance cost, quality, and responsiveness with a hybrid IT support strategy. Perfect for IT managers, operations leads, and business owners considering global help desk solutions.

Cyber Security Legal Framework in Nepal.pptxGhimire B.R.