Research

PIE-NeRF Serves Up a New Slice: Physics-Based NeRFs

Michael Rubloff

Michael Rubloff

Nov 24, 2023

Email
Copy Link
Twitter
Linkedin
Reddit
Whatsapp
PIE-NeRF
PIE-NeRF

Just when we thought the week couldn't get more exciting in the world of physics-based radiance fields, we're introduced to another groundbreaking development: PIE-NeRF (Physics-based Interactive Elastodynamics with Neural Radiance Fields). Coming hot on the heels of the remarkable PhysGaussian paper, PIE-NeRF represents another leap forward, this time in the domain of Neural Radiance Fields (NeRFs).

PIE-NeRF emerges as a standout innovation, shifting the paradigm from traditional mesh-based structures to a pioneering meshless technique for simulating physical dynamics. This leap is particularly noteworthy, considering the intensive computational demands typically associated with 3D modeling.

It's been a busy time for Yutao Feng, Xuan Li, Yin Yang, and Chenfanfu Jiang, all of whom are authors on both papers!

PIE-NeRF integrates physics-based simulations with the NeRF scene. This integration is akin to infusing the static scene with the laws of physics - gravity, force, and motion - allowing for the realistic simulation of dynamic properties. Whether it's a swaying plant or a flowing cloth, PIE-NeRF calculates how these objects would move and interact in a real-world setting.

PIE-NeRF starts its journey by reimagining how we capture 3D scenes. Unlike conventional methods that rely on rigid mesh structures, NeRF uses a fluid, continuous field, encoded within a neural network, to understand a scene's color, texture, and geometry from 2D images. This neural encoding of space lays the groundwork for the dynamic animations PIE-NeRF aims to achieve and is the canvas on which PIE-NeRF will paint its dynamic animations. Like many other papers, PIE-NeRF employs NVIDIA's Instant NGP to build on top off.

Transitioning to 3D, PIE-NeRF transcends static models to foster interactive, dynamic scenes. Here, the innovation truly shines: the framework calculates physical properties and movements within NeRF's continuous space, sidestepping the need for meshes. This meshless method is not just a technical novelty but a strategic advantage, enabling simulations to occur independently of sampling resolution, which dramatically lowers computational demands. PIE-NeRF enables physical properties and movements to be calculated within this continuous space, without the need for a predefined mesh.

Central to PIE-NeRF's operation is Quadratic Generalized Moving Least Squares (Q-GMLS), the engine that powers the framework. This advanced method interprets and simulates physical behaviors like elasticity and deformation within NeRF's space. Its ability to handle complex, non-linear deformations—where traditional linear methods stumble—marks a significant advancement, especially in avoiding 'locking artifacts', a common issue in linear simulations under extreme deformation scenarios.

In processing complex simulations, PIE-NeRF smartly employs spatial reduction via Voronoi partitioning. This approach is akin to strategically positioning sensors across a field, each overseeing a specific region. It ensures that computational resources are concentrated where dynamic interactions are most pronounced, thus fostering real-time interactions and simulations, without compromising the quality of the final output.

PIE-NeRF's true prowess is displayed when it integrates physics-based simulations with NeRF scenes. This integration is like breathing life into static models, as the laws of physics—gravity, force, motion—are applied, enabling realistic simulation of dynamic properties. Whether it's a swaying plant or flowing cloth, PIE-NeRF accurately predicts their real-world movements and interactions.

PIE-NeRF renders the dynamically altered scenes. Using deformation information, it precisely maps changes back to the NeRF model, ensuring that rendered images maintain high fidelity, even under significant deformations. Here, the interactive element comes into play: users can manipulate the scene, apply forces, and witness the immediate impact of their actions, all in real-time.

As PIE-NeRF continues to evolve, we eagerly await the release of its code and video overviews. These resources will undoubtedly provide deeper insights into its capabilities and applications. Powered by a 3090 GPU, PIE-NeRF is not just a testament to current technological advancements but a beacon for future innovations in digital representation.

[Editor's Note: This article will be updated with additional resources and demonstrations of PIE-NeRF as they become available.]

Featured

Featured

Featured

Platforms

OpenNeRF added to Nerfstudio

OpenNeRF is the latest method to be supported by Nerfstudio.

Michael Rubloff

May 24, 2024

Platforms

OpenNeRF added to Nerfstudio

OpenNeRF is the latest method to be supported by Nerfstudio.

Michael Rubloff

May 24, 2024

Platforms

OpenNeRF added to Nerfstudio

OpenNeRF is the latest method to be supported by Nerfstudio.

Michael Rubloff

Platforms

PlayCanvas's SuperSplat Updated with PWA support

PlayCanvas's Supersplat has continued to receive additional updates. This time it's coming with a big boost to performance, yet again.

Michael Rubloff

May 24, 2024

Platforms

PlayCanvas's SuperSplat Updated with PWA support

PlayCanvas's Supersplat has continued to receive additional updates. This time it's coming with a big boost to performance, yet again.

Michael Rubloff

May 24, 2024

Platforms

PlayCanvas's SuperSplat Updated with PWA support

PlayCanvas's Supersplat has continued to receive additional updates. This time it's coming with a big boost to performance, yet again.

Michael Rubloff

Research

Reflecting on NeRF-Casting

Late last year we looked at Uni-SDF which introduced dual radiance fields to better represent reflections in a scene. However, I just happened to see NeRF-Casting on Github a little while ago

Michael Rubloff

May 23, 2024

Research

Reflecting on NeRF-Casting

Late last year we looked at Uni-SDF which introduced dual radiance fields to better represent reflections in a scene. However, I just happened to see NeRF-Casting on Github a little while ago

Michael Rubloff

May 23, 2024

Research

Reflecting on NeRF-Casting

Late last year we looked at Uni-SDF which introduced dual radiance fields to better represent reflections in a scene. However, I just happened to see NeRF-Casting on Github a little while ago

Michael Rubloff

Platforms

Scaniverse arrives on Android

Gaussian Splatting platform, Scaniverse, is now available on Android.

Michael Rubloff

May 21, 2024

Platforms

Scaniverse arrives on Android

Gaussian Splatting platform, Scaniverse, is now available on Android.

Michael Rubloff

May 21, 2024

Platforms

Scaniverse arrives on Android

Gaussian Splatting platform, Scaniverse, is now available on Android.

Michael Rubloff

Trending articles

Trending articles

Trending articles

Platforms

Google CloudNeRF: Zip-NeRF and CamP in the Cloud

It doesn't seem like a lot of people know this, but you can run CamP and Zip-NeRF in the cloud, straight through Google and it's actually super easy. It’s called CloudNeRF.

Michael Rubloff

May 8, 2024

Platforms

Google CloudNeRF: Zip-NeRF and CamP in the Cloud

It doesn't seem like a lot of people know this, but you can run CamP and Zip-NeRF in the cloud, straight through Google and it's actually super easy. It’s called CloudNeRF.

Michael Rubloff

May 8, 2024

Platforms

Google CloudNeRF: Zip-NeRF and CamP in the Cloud

It doesn't seem like a lot of people know this, but you can run CamP and Zip-NeRF in the cloud, straight through Google and it's actually super easy. It’s called CloudNeRF.

Michael Rubloff

Research

Gaustudio

Gaussian Splatting methods have continued to pour in over the first three months of the year. With the rate of adoption, being able to merge and compare these methods, shortly after their release would be amazing.

Michael Rubloff

Apr 8, 2024

Research

Gaustudio

Gaussian Splatting methods have continued to pour in over the first three months of the year. With the rate of adoption, being able to merge and compare these methods, shortly after their release would be amazing.

Michael Rubloff

Apr 8, 2024

Research

Gaustudio

Gaussian Splatting methods have continued to pour in over the first three months of the year. With the rate of adoption, being able to merge and compare these methods, shortly after their release would be amazing.

Michael Rubloff

Tools

splaTV: Dynamic Gaussian Splatting Viewer

Kevin Kwok, perhaps better known as Antimatter15, has released something amazing: splaTV.

Michael Rubloff

Mar 15, 2024

Tools

splaTV: Dynamic Gaussian Splatting Viewer

Kevin Kwok, perhaps better known as Antimatter15, has released something amazing: splaTV.

Michael Rubloff

Mar 15, 2024

Tools

splaTV: Dynamic Gaussian Splatting Viewer

Kevin Kwok, perhaps better known as Antimatter15, has released something amazing: splaTV.

Michael Rubloff

Research

The MERF that turned into a SMERF

For the long time readers of this site, earlier this year, we looked into Google Research's Memory Efficient Radiance Fields (MERF). Now, they're back with another groundbreaking method: Streamable Memory Efficient Radiance Fields, or SMERF.

Michael Rubloff

Dec 13, 2023

Research

The MERF that turned into a SMERF

For the long time readers of this site, earlier this year, we looked into Google Research's Memory Efficient Radiance Fields (MERF). Now, they're back with another groundbreaking method: Streamable Memory Efficient Radiance Fields, or SMERF.

Michael Rubloff

Dec 13, 2023

Research

The MERF that turned into a SMERF

For the long time readers of this site, earlier this year, we looked into Google Research's Memory Efficient Radiance Fields (MERF). Now, they're back with another groundbreaking method: Streamable Memory Efficient Radiance Fields, or SMERF.

Michael Rubloff

Featured

Featured

Platforms

Google CloudNeRF: Zip-NeRF and CamP in the Cloud

It doesn't seem like a lot of people know this, but you can run CamP and Zip-NeRF in the cloud, straight through Google and it's actually super easy. It’s called CloudNeRF.

Michael Rubloff

May 8, 2024

Platforms

Google CloudNeRF: Zip-NeRF and CamP in the Cloud

It doesn't seem like a lot of people know this, but you can run CamP and Zip-NeRF in the cloud, straight through Google and it's actually super easy. It’s called CloudNeRF.

Michael Rubloff

May 8, 2024

Platforms

Google CloudNeRF: Zip-NeRF and CamP in the Cloud

Michael Rubloff

May 8, 2024

Research

Gaustudio

Gaussian Splatting methods have continued to pour in over the first three months of the year. With the rate of adoption, being able to merge and compare these methods, shortly after their release would be amazing.

Michael Rubloff

Apr 8, 2024

Gaustudio

Research

Gaustudio

Gaussian Splatting methods have continued to pour in over the first three months of the year. With the rate of adoption, being able to merge and compare these methods, shortly after their release would be amazing.

Michael Rubloff

Apr 8, 2024

Gaustudio

Research

Gaustudio

Michael Rubloff

Apr 8, 2024

Gaustudio

Tools

splaTV: Dynamic Gaussian Splatting Viewer

Kevin Kwok, perhaps better known as Antimatter15, has released something amazing: splaTV.

Michael Rubloff

Mar 15, 2024

SplaTV

Tools

splaTV: Dynamic Gaussian Splatting Viewer

Kevin Kwok, perhaps better known as Antimatter15, has released something amazing: splaTV.

Michael Rubloff

Mar 15, 2024

SplaTV

Tools

splaTV: Dynamic Gaussian Splatting Viewer

Michael Rubloff

Mar 15, 2024

SplaTV