newton-physics/newton
Oct 2025 – Oct 2025
1 Month active
Languages Used
C++Python
Technical Skills
3D Model ImportCUDAColor NormalizationComputer GraphicsGPU ComputingMJCF Parsing
Diego Ferigo
PROFILE
Diego Ferigo
Worked on the newton-physics/newton repository to enhance real-time visualization workflows by implementing GPU-based last frame capture in ViewerGL. Leveraging OpenGL Pixel Buffer Objects with CUDA interoperability, the solution enabled direct GPU-to-GPU frame transfers, reducing CPU overhead and improving capture efficiency. Addressed a critical issue in MJCF model rendering by fixing texture color handling, ensuring the main color is correctly applied when textures are present and normalizing color values for consistent visual output. The work demonstrated strong proficiency in C++, Python, and GPU computing, focusing on optimizing rendering pipelines and improving the accuracy and performance of 3D model visualization.
Overall Statistics
Feature vs Bugs
50%Features
Repository Contributions
2Total
Bugs
1
Commits
2
Features
1
Lines of code
86
Activity Months1
Your Network
49 peopleSame Organization
@rai-inst.com1
Shared Repositories
48
Work History
October 2025
2 Commits • 1 Features
Oct 1, 2025October 2025 monthly work summary for repository newton-physics/newton. Key highlights include GPU-based last frame capture in ViewerGL using OpenGL Pixel Buffer Objects (PBO) with CUDA interoperability to enable GPU-to-GPU frame transfer, reducing CPU overhead and enabling efficient capture workflows. Also addressed a critical texture issue in MJCF models: fixed color handling by correctly applying the main color when textures are present and normalizing color values to improve visual fidelity and consistency.
2 Commits • 1 Features
Oct 1, 2025October 2025 monthly work summary for repository newton-physics/newton. Key highlights include GPU-based last frame capture in ViewerGL using OpenGL Pixel Buffer Objects (PBO) with CUDA interoperability to enable GPU-to-GPU frame transfer, reducing CPU overhead and enabling efficient capture workflows. Also addressed a critical texture issue in MJCF models: fixed color handling by correctly applying the main color when textures are present and normalizing color values to improve visual fidelity and consistency.
October 2025
Activity
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Quality Metrics
Correctness90.0%
Maintainability90.0%
Architecture80.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++Python
Technical Skills
3D Model ImportCUDAColor NormalizationComputer GraphicsGPU ComputingMJCF ParsingOpenGLPython
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