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?
Congrats on the paper! Any chance the code will be released?
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).
Re: the 4FPS example, if one renders a VR180 view as an equirectangular image and lets the headset handle the 6DOF head movement, then 4FPS is plenty. Especially if there’s one render per-eye. 110% if there are no objects within a meter of the ego camera.
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.
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.
Does anyone else look forward to a game that lets you transform your house or neighbor into a playable level with destructible objects? How far are we from recognizing the “car” and making it drivable, or the “tree” and making it choppable?
I work in the rendering and gaming industry and also run a 3D scanning company. I have similarly wished for this capability, especially the destructability part. What you speak of is still pretty far off for several reasons:
-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.
My parents had a floor plan of our house drawn up for some reason, and when I was in late middle school I found it and modeled the house in the hammer editor so my friends and I could play Counter Strike source in there.
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.
An interactive game is much more than just rendering. You need object separation, animation, collision, worldspace, logic, and your requirement of destructibility takes it to a completely different level.
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.
I dreamed of that since being a kid, so for nearly three decades now. It's been entirely possible even then - it was just a matter of using enough elbow grease. The problem is, the world is full of shiny happy people ready to call you a terrorist, assert their architectural copyright, or bring in the "creepiness factor", to shut down anyone who tries this.
What's the state of the art right now that can be run on my laptop from a set of photos? I want to play with NERFs, starting by generating one from a bunch of photos of my apartment, so I can then fly around the space virtually.
Absolute noob question that I'm having a hard time understading:
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?
There are two aspects in the difference between NeRF and Gaussian Splatting :
- 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.
NeRF does glass, fog, reflections, and some furs better than gsplat. Gsplat does normal surfaces as well or better than NeRF, and gsplat also provides explicit geometry (a point cloud). NeRF models the entire image rendering function while gsplats only model typical surfaces.
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.
It's just a completely different paradigm of rendering and it's not clear which one will be dominant in the future. Gaussian splats are usually dependent on initialisation from point cloud, which makes whole process much more compliacated.
turkihaithem|1 year ago
aerodog|1 year ago
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?
refibrillator|1 year ago
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).
choppaface|1 year ago
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.
dr_dshiv|1 year ago
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.
unknown|1 year ago
[deleted]
ttul|1 year ago
totalview|1 year ago
-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.
naet|1 year ago
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.
orbital-decay|1 year ago
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.
w-m|1 year ago
TeMPOraL|1 year ago
antihero|1 year ago
55555|1 year ago
sorenjan|1 year ago
https://docs.nerf.studio/
lxe|1 year ago
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?
GistNoesis|1 year ago
- 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.
choppaface|1 year ago
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.
zlenyk|1 year ago
ofou|1 year ago
tmilard|1 year ago
So many laboratories and software dev have given a shot at this. None have yet won.
Success often lies in small (but important ) details...
unknown|1 year ago
[deleted]
thediversemark|1 year ago
[deleted]