ShaRF: Create a 3D Model of an Object Using Just a Single Image

Neural scene representation from a single image is a really complex problem. The "end goal" is to be able to take a picture of a real-life object, and translate this picture into a 3D scene. It implies that the model understands a whole 3-dimensional scene, or real-life scene, using information from a single picture.

[1] Rematas, K., Martin-Brualla, R., and Ferrari, V., "ShaRF: Shape-conditioned Radiance Fields from a Single View", (2021), https://arxiv.org/abs/2102.08860​

[2] Project website and link to code for ShaRF: http://www.krematas.com/sharf/index.html​

[3] Mildenhall et al., NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis, (2020), https://www.matthewtancik.com/nerf​

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0:28​ - Paper explanation & examples

4:42​ - Conclusion

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just imagine how cool it will be to just

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take a picture of an object and have it

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in

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3d to insert in the movie or video game

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you are creating

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this is what google is working on

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neural scene representation from a

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single image is a really complex problem

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the end goal is to be able to take a

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picture from a real life object

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and translate this picture into a 3d

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scene

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it implies that the model understands a

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whole three-dimensional

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scene or real-life scene using

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information from a single picture

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and this is sometimes hard even for

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humans

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where the colors or shadows tricks our

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eyes

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oh and not only that it needs to

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understand the depth

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in the image which is already a

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challenging task to do but it also needs

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to reconstruct the objects with the

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right materials and texture

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so it can look real you can just imagine

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how cool it will be to just take a

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picture of an

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object and have it in 3d to insert in

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the movie or video game you are creating

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or in a 3d scene for an illustration

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well

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i am not the only one thinking about all

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the possibilities this type of model

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could create

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since google researchers are looking

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into this in their new paper

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sherf shape condition regions field

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from a single view and you're seeing the

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results they could produce since the

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start of the video

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note that for each of these results you

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saw they only used one picture

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taken from any angle it was then sent to

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the model in order to produce these

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results

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which are incredible to me when you

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think of the complexity of the task in

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all the possible parameters to take into

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consideration

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just regarding the initial picture such

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as the lighting

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the resolution the size the angle or

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viewpoint the location of the object in

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the image

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and etc if you're like me you may be

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wondering

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how are they doing that okay so i lied a

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little

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they do not only take the image as

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inputs to the network

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but they also take the camera parameters

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to help the process

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the algorithm learn the function that

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converts

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these 3d points and 3d viewpoints into

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an

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rgb color as well as a density value for

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each of this point

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providing enough information to render

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the scene from any viewpoints

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later on this is called a radiance field

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taking positions in its viewing

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direction as inputs to output this color

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and volume density value

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for each of these points it's very

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similar to what nerf does

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which is a paper i already covered on my

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channel

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basically in the nerf case the regions

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field function

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is done using a neural network trained

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on images and the internet output

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this implies that they need a large

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number of images for each scene

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as well as training a different network

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for each of these scenes

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making the process very costly and

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inefficient so the goal is to find a

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better way to have this needed radiance

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field

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composed of rgb and density values to

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then

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render the object in 3d in novel views

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in order to have the needed information

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to create such a radiance field

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they used what they call a shape network

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that maps a latent code of the image

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into a 3d

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shape made of voxels voxels are just the

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same as pixels but in three dimensional

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space

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and the latent code in question is

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basically all the useful information for

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the shape of the object in the image

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this condensed shape information is

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found using a neural network composed of

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fully connected layers

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and followed by convolutions which are

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powerful architectures for computer

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vision applications

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since convolutions have two main

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properties

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they are invariant to translations and

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use the local properties of the images

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then it takes this latent code to

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produce this first

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3d shape estimation you will think that

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we are done here

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but it's not the case this is just the

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first step

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then as we discussed we need the

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radiance field

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of this representation using here an

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appearance network

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here again it uses a similar latent code

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but for the appearance

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as well as the 3d shapes we just found

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as inputs

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to produce this regions field using

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another network

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referred here as f then this radians

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field

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can finally be used with the camera

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parameters information

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to produce this final render of the

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object

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in novel views

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this was just an overview of this new

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paper i strongly recommend reading the

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paper

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linked in the description below the code

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is unfortunately

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not available right now but i contacted

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one of the authors

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and he said that it will be available in

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a couple of weeks

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so stay tuned for that please leave a

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