Back To Projects

Neural Radiance Fields (NeRF) for 3D Visualization Rendering Based on 2D Images

Joann C.

Reconstructing 3D scenes and being able to interact with them in virtual environments used to be something beyond our capabilities but now with the use of Neural Radiance Fields (NeRF) and Instant Neural Graphic Primitives (InstantNGP) we are able to create a world in a virtual space, opening the gateways to applications in object detection and generation, augmented reality, medical imaging, and more.


We are living in a 3D world, but oftentimes we visualize things in 2D scopes like in pictures and videos of different objects. The world of 3D visualization has been constantly evolving over the past few years, still growing with the introduction of cutting-edge computer vision and deep learning methods. Reconstructing 3D scenes and being able to interact with them in virtual environments used to be something beyond our capabilities but now with the use of Neural Radiance Fields (NeRF) and Instant Neural Graphic Primitives (InstantNGP) we are able to create a world in a virtual space, opening the gateways to applications in object detection and generation, augmented reality, medical imaging, and more. Access to these methods will allow for more immersive development by creators, hands-on educational resources, and more experimentation and research, stimulating future applications and techniques.

Explore More!

Joann C.
Ivan Felipe Rodriguez
PhD Candidate at Brown

Related Projects

Using Machine Learning Models to Analyze the Aerodynamic Properties of Airfoils

This research posits that leveraging artificial intelligence could significantly reduce financial and computational costs while identifying optimal airfoil geometries.
Aaron W.
Mentored by Ronil Synghal
Vibration Analysis

This paper covers differences in accuracies of an artificial intelligence model that classifies different sets of sensor outputs (in this case, machinery shaft vibrations) collected by sensors into varying levels of weight on the shaft.
Akshay N.
Mentored by
Predicting the Presence of Autism Spectrum Disorder Based on Eye-Tracking Scan Path Images

This research explores using eye-tracking data and machine learning to predict Autism Spectrum Disorder (ASD), achieving 74.6% accuracy with logistic regression, highlighting eye-tracking's potential for early, non-invasive ASD diagnosis and its applicability to other neurological conditions.
Sara C.
Mentored by Emily Broadhurst