HybridNeRF: Efficient Neural Rendering
haithemturki.com
haithemturki.com
Also I’d be curious to hear, what are you excited about in terms of future research ideas?
Personally I’m excited by the trend of eliminating the need for traditional SfM preprocessing (sparse point clouds via colmap, camera pose estimation, etc).
IMHO it's a really exciting time to be in the neural rendering / 3D vision space - the field is moving quickly and there's interesting work across all dimensions. My personal interests lean towards large-scale 3D reconstruction, and to that effect eliminating the need for traditional SfM/COLMAP preprocessing would be great. There's a lot of relevant recent work (https://dust3r.europe.naverlabs.com/, https://cameronosmith.github.io/flowmap/, https://vggsfm.github.io/, etc), but scaling these methods beyond several dozen images remains a challenge. I’m also really excited about using learned priors that can improve NeRF quality in underobserved regions (https://reconfusion.github.io). IMO using these priors will be super important to enabling dynamic 4D reconstruction (since it’s otherwise unfeasible to directly observe every space-time point in a scene). Finally, making NeRF environments more interactive (as other posts have described) would unlock many use cases especially in simulation (ie: for autonomous driving). This is kind of tricky for implicit representations (like the original NeRF and this work), but there have been some really cool papers in the 3D Gaussian space (https://xpandora.github.io/PhysGaussian/) that are exciting.
I'm interested in using Nerf to generate interpolated frames from a set of images. I want to do a poor man's animation. I'm interested in finding Nerf with code but it feels hard to find. Do you know of a good starting point? I tried running nerfstudio and the results weren't great.
If you are trying to capture dynamics, most of the Nerfstudio methods are geared towards static captures and will give poor results for scenes with movement. There are many dynamic NeRF works out there - for example https://dynamic3dgaussians.github.io/ and https://github.com/andrewsonga/Total-Recon both provide code if you want to play around. With that being said, robust 4D reconstruction is still very much an open research problem (especially when limited to monocular RGB data / casual phone captures). I'd expect a lot of movement in the space in the months/years to come!
I'm trying to recreate my experiences with claymation when I was a kid. I want to take a picture of an object, like a lego figure, and then move it slightly, take another picture, then move the figure again slightly. Once I have some frames, I want to use a NERFs to interpolate between those frames.
When I was young and doing claymation, I would move the figure, shoot two frames, and do that 12 times per second of film. And, the lights I used were often so hot, that my clay would melt and alter the figure. It was a chore.
I thought I could capture fewer in-between frames and let the NERF figure out the interpolation, and perhaps get some weird side effects. Especially if it hallucinates.
I'm not sure if a NERF is the right approach, but it seems like a good starting point.
Thank you.
In terms of publicly available code, I think Stable Video Diffusion can do frame interpolation (https://stability.ai/news/introducing-stable-video-diffusion...), but I haven't tried it myself.
From my experience, NERF works great, but depends on highly accurate camera location information. Unless the VR device has this baked in, one must run a Colmap-style or SFM-style process to generate those camera extrinsics. Is there anything special HybridNeRF does around this?
Can regular phones capture the data required? How to get into this, as a hobbyist? I’m interested in the possibilities of scanning coral reefs and other ecological settings.
So your motivating problem does not exist.
More FPS is better, and yes we all do want to find a hybrid of NeRF and splats that works well, but then you should emphasize your theoretical and experimental contributions. Flatly claiming 4FPS doesn’t work is specious to most readers. Even Deva knows this is being too aggressive for a paper like this.
The main exception I can think of is in racing simulators, it's already common for the developers of those to drive LiDAR cars around real-world tracks and use that data to build a 1:1 replica for their game. NeRF might be a natural extension of that if they can figure out a way to combine it with dynamic lighting and weather conditions.
Recognising objects for what they are has only recently become somewhat possible. Separating them in a 3D scan is still pretty much impossible.
-No Collision/poor collision on NERFs and GS: to have a proper interactive world, you usually need accurate character collision so that your character or vehicle can move along the floor/ground (as opposed to falling thru it) run into walls, go through door frames, etc. NERFs suffer from the same issues as photogrammetry in that they need “structure from motion” (COLMAP or similar) to give them a mesh or 3-D output that can be meshed for collision to register off of. The mesh from reality capture is noisy, and is not simple geometry. Think millions of triangles from a laser scanner or camera for “flat” ground that a video game would use 100 triangles for.
-Scanning: there’s no scanner available that provides both good 3-D information and good photo realistic textures at a price people will want to pay. Scanning every square inch of playable space in even a modest sized house is a pain, and people will look behind the television, underneath the furniture and everywhere else that most of these scanning videos and demos never go. There are a lot of ugly angles that these videos omit where a player would go.
-Post Processing: of you scan your house or any other real space, you will have poor lighting unless you took the time to do your own custom lighting and color setup. That will all need to be corrected in post process so that you can dynamically light your environment. Lighting is one of the most next generation things that people associate with games and you will be fighting prebaked shadows throughout the entire house or area that you have scanned. You don’t get away from this with NERFs or gaussian splats, because those scenes also have prebaked lighting in them that is static.
Object Destruction and Physics: I Love the game teardown, and if you want to see what it’s like to actually bust up and destroy structures that have been physically scanned, there is a plug-in to import reality capture models directly into the game with a little bit of modding. That said, teardown is voxel based, and is one of the most advanced engines that has been built to do such a thing. I have seen nothing else capable of doing cool looking destruction of any object, scanned or 3D modeled, without a large studio effort and a ton of optimization.
Lighting is the big issue, IMO. As soon as you want any kind of interactivity besides moving the camera you need dynamic lighting. The problem is you're going to have to mix the captured absolutely perfect real-world lighting with extremely approximate real-time computed lighting (which will be much worse than offline-rendered path tracing, which still wouldn't match real-world quality). It's going to look awful. At least, until someone figures out a revolutionary neural relighting system. We are pretty far from that today.
Scale is another issue. Two issues, really, rendering and storage. There's already a lot of research into scaling up rendering to large and detailed scenes, but I wouldn't say it's solved yet. And once you have rendering, storage will be the next issue. These scans will be massive and we'll need some very effective compression to be able to distribute large scenes to users.
The collision detection problem is related heavily to having clean 3D as mentioned above. My company is doing development on computing collision on reality capture right now in a clean way and I would be interested in any thoughts you have. We are chunking collision on the dataset at a fixed distance from the player character (can’t go too fast in a vehicle or it will outpace the collision and fall thru the floor) and have a tunable LOD that influences collision resolution.
Frankly I’m surprised that I can’t easily make crude 3D models of spaces with a simple app presently. It seems well within the capabilities of the hardware and software.
In other words, if you've got a consistent 3D geometric map of the house with textures, then you can do a pass in the morning with only daylight, midday only daylight, late afternoon only daylight, and then one at night with artificial light.
If you're dealing with textures that map onto identical geometries (and assume no objects move during the day), it seems like it ought to be relatively straightforward to train AI's to produce a flat unlit texture version, especially since you can train them on easily generated raytraced renderings. There might even be straight-up statistical methods to do it.
So I think it not the lighting itself that is the biggest problem -- it's having the clean consistent geometries in the first place.
It wasn't very well done but I figured out how to make the basic walls and building, add stairs, add some windows, grab some pre existing props like simple couches beds and a TV, and it was pretty recognizable. After adding a couple ladders to the outside so you could climb in the windows or on the roof the map was super fun just as a map, and doubly so since I could do things like hide in my own bedroom closet and recognize the rooms.
Took some work since I didn't know how to do anything but totally worth it. I feel like there has to be a much more accessible level editor in some game out there today, not sure what it would be though.
I thought my school had great architecture for another map but someone rightfully convinced me that would be a very bad idea to add to a shooting game. So I never made any others besides the house.
NeRF is not that, it's just a way to represent and render volumetric objects. It's like 10% of what makes a game. Eventually, in theory, it might be possible to make NeRFs or another similar representation animated, interactive, or even entirely drivable by an end-to-end model. But the current state is so far from it that it isn't worth speculating about.
What you want is doable with classic tools already.
In practice, why NeRF instead of Gaussian Splatting? I have very limited exposure to either, but a very cursory search on the subject yields a "it depends on the context" answer. What exact context?
- The first aspect concern how they solve the light rendering equation :
NeRF has more potential for rendering physical quality but is slower.
NeRF use raycasting. Gaussian Splatting project and draw gaussians directly in screen space.
Each have various rendering artefacts. One distinction is in handling light reflections. When you use raycasting, you can bounce your ray on mirror surfaces. Where as gaussian splatting, like alice in wonderland creates a symmetric world on the other side of the mirror (and when the mirror surface is curved, it's hopeless).
Although many NeRF don't implement reflections as a simplification, they can handle them almost natively.
Alternatively, NeRF is a volumetric representation, whereas Gaussian Splatting has surfaces baked in : Gaussian Splats are rendered in order front to back. This mean that when you have two thin objects one behind the other, like the two sides of a book, Gaussian splatting will be able to render the front and hide the back whereas NeRF will merge front and back because volumetric element are transparent. (Though in NeRF with spherical harmonics the Radiance Field direction will allow to cull back from front based on the viewing angle).
- The second aspect of NeRF vs Gaussian Splatting, is the choice of representation :
NeRF usually use a neural network to store the scene in a compressed form. Whereas Gaussian Splatting is more explicit and uncompressed, the scene is represented in a sort of "point cloud" fashion. This mean that if your scene has potential for compression, like repetitive textures or objects, then the NeRF will make use of it and hallucinate what's missing. Whereas gaussian splat will show holes.
Of course like this article is about, you can hybridize them.
This work (and others e.g. https://creiser.github.io/binary_opacity_grid/ ) attempt to blend the raytracing aspect of NeRF with the explicit surface aspect of gsplats.
One key non-research problem is that gsplats can render on mobile devices / headsets using vanilla WebGL APIs. But approaches like this paper require CUDA (and also apparently a top-shelf desktop GPU). If Apple and others (mostly Apple has been sandbagging tho) provided better support for WebGPU or an alternative API then NeRF research would be dramatically more impactful versus gsplats. The popularity of gsplats is largely due to its accessibility.
So many laboratories and software dev have given a shot at this. None have yet won.
Success often lies in small (but important ) details...