NVIDIA Neuralangelo: 3D Scans from Images | Llm nlp | Llm Machine Learning | Llm ai Significato | Turtles AI

The new technique #Neuralangelo from #NVIDIA has finally been #released to the public on #GitHub and you can find its #repository here. It uses #neural #rendering and implicit surfaces to accurately reconstruct detailed #3D #models from #images and #video. Outperforms other learning-based methods. Enables high-fidelity 3D scanning for applications like digitizing artefacts or virtual try-on. A new computer vision technique called Neuralangelo allows for high-fidelity 3D surface reconstruction from images and videos. Developed by researchers at NVIDIA, Neuralangelo combines neural rendering with implicit surface representations to generate detailed and accurate 3D models. The method was presented in a paper at the IEEE Conference on Computer Vision and Pattern Recognition 2023. Neuralangelo represents surfaces using coordinate-based multilayer perceptrons that map 3D coordinates to occupancy values. This allows for representing complex geometry in a memory-efficient way compared to voxel grids or meshes. To reconstruct surfaces, Neuralangelo leverages differentiable rendering techniques to compare rendered images of the implicit surface to input images. By optimizing the MLP weights to minimize the difference, the surface geometry can be recovered. Neuralangelo also extracts appearance information from images to colour the surface. Experiments demonstrate Neuralangelo's ability to reconstruct highly detailed geometry like clothes and hair from multi-view images and video. The technique outperforms other learning-based 3D reconstruction methods in terms of accuracy. The code and data processing scripts are available in an open-source repository to facilitate further research. Overall, Neuralangelo enables high-fidelity 3D scanning of real-world scenes from images and video. It could enable applications like digitizing cultural artefacts or creating digital doubles for virtual try-on. The combination of neural rendering and implicit surfaces is a promising direction for 3D reconstruction. Highlights:

• - Uses implicit surface representation with coordinate-based MLPs to represent complex geometry efficiently.
• - Leverages differentiable rendering to optimize surface to match input views.
• - Extracts colour information from images to texture the surface.
• - Achieves state-of-the-art accuracy in reconstructing detailed geometry like clothes and hair.
• - Open-source implementation available to facilitate research.

This technique has applications in digitizing real-world objects for VR/AR and creating digital avatars. It will be interesting to see how Neuralangelo performs on highly reflective or transparent objects. What other applications could benefit from high-fidelity image-based 3D scanning? Let us know your thoughts!