In this episode of Computer Vision Decoded, we bring to you a live recording of Jared Heinly presentation on the evolution of image based 3D reconstruction. This recording was from a Computer Vision Decoded meetup in Pittsburgh with a visual component. If you would like to tune in with the visuals, we recommend watching the episode on our YouTube channel: https://youtu.be/Gwib5IcTKHI

Follow:

• Jared on X: https://x.com/JaredHeinly
• Jonathan on X: https://x.com/jonstephens85

This episode is brought to you by EveryPoint. Learn more about how EveryPoint is building an infinitely scalable data collection and processing platform for the next generation of spatial computing applications and services. Learn more at https://www.everypoint.io

In this episode of Computer Vision Decoded, our hosts Jonathan Stephens and Jared Heinly are joined by Ruilong Li, a researcher at NVIDIA and key contributor to both Nerfstudio and gsplat, to dive deep into 3D Gaussian Splatting. They explore how this relatively new technology works, from the fundamentals of gaussian representations to the optimization process that creates photorealistic 3D scenes. Ruilong explains the technical details behind gaussian splatting, and discusses the development of the popular gsplat library. The conversation covers practical advice for capturing high-quality data, the iterative training process, and how Gaussian splatting compares to other 3D representations like meshes and NeRFs.

Links:

• gsplat: https://github.com/nerfstudio-project/gsplat
• Nerfstudio: https://docs.nerf.studio/

Follow:

• Ruilong on X: https://x.com/ruilong_li
• Jared on X: https://x.com/JaredHeinly
• Jonathan on X: https://x.com/jonstephens85

This episode is brought to you by EveryPoint. Learn more about how EveryPoint is building an infinitely scalable data collection and processing platform for the next generation of spatial computing applications and services. Learn more at https://www.everypoint.io

In this episode of Computer Vision Decoded, hosts Jonathan Stephens and Jared Heinly explore the various types of cameras used in computer vision and 3D reconstruction. They discuss the strengths and weaknesses of smartphone cameras, DSLR and mirrorless cameras, action cameras, drones, and specialized cameras like 360, thermal, and event cameras. The conversation emphasizes the importance of understanding camera specifications, metadata, and the impact of different lenses on image quality. The hosts also provide practical advice for beginners in 3D reconstruction, encouraging them to start with the cameras they already own.

Takeaways

• Smartphones are versatile and user-friendly for photography.
• RAW images preserve more data than JPEGs, aiding in post-processing.
• Mirrorless and DSLR cameras offer better low-light performance and lens flexibility.
• Drones provide unique perspectives and programmable flight paths for capturing images.
• 360 cameras allow for quick scene capture but may require additional processing for 3D reconstruction.
• Event cameras capture rapid changes in intensity, useful for robotics applications.
• Thermal and multispectral cameras are specialized for specific applications, not typically used for 3D reconstruction.
• Understanding camera metadata is crucial for effective image processing.
• Choosing the right camera depends on the specific needs of the project.
• Starting with a smartphone is a low barrier to entry for beginners in 3D reconstruction.

This episode is brought to you by EveryPoint. Learn more about how EveryPoint is building an infinitely scalable data collection and processing platform for the next generation of spatial computing applications and services: https://www.everypoint.io