SDF

Question 1

DeepSDF - what is the input and the output of the network

Answer 1

Input - latent vector related to a certain shape and a query 3D point in space
Output - the SDF value of the 3D point in space for that shape

Question 2

DeepSDF - why are they not using an encoder-decoder framework

Answer 2

They say that they’re not sure of the usefulness of the encoder and want to use computational resources better.

Question 3

DeepSDF - what is a posterior. And what is being done with it in the paper?

Answer 3

The probability for an even to occur given an evidence for something.
They take the posterior and break it to the code probability (prior) multiplied with the multiplication of the SDF from each point.

Question 4

DeepSDF - what is the prior distribution of the latent codes

Answer 4

Gaussian distribution of means = 0 and the same variance in each dimension (while each covariance is 0)

Question 5

DeepSDF - what is the paper expression of the SDF likelihood

Answer 5

exp(-loss(NN(latent_code, 3D point), SDF))

The exponent of minus loss.
The loss is between the NN to the SDF in a 3D point.
The input to the NN is the latent code and that 3D point.

Question 6

What is TSDF

Answer 6

Truncated SDF

Question 7

DeepSDF - How to get the TSDF

Answer 7

Convert the mesh into voxels and then calculate the SDF?

Question 8

ASDF - what is ASDF

Answer 8

Articulated SDF

Question 9

ASDF - What is their contribution?

Answer 9

1 Separate the code area for shape and for articulation
2 No prior assumption on the geometry or joint type, location and range.
3 Test-time adaptation algo -> adjust the model in inference.

Question 10

ASDF - What are their findings?

Answer 10

That their model can generalise well to out-of-distribution and unseen data (partial point cloud + depth images).

Question 11

Re-param pix int - What is re-parametrisation of the pixel integral?

Answer 11

In rendering optimisation we integrate the rays that go through a pixel. Reparametrisation is to reformulate the integral so it would be done more robustly.

Question 12

Re-param pix int - Where is the problem they are trying to fix?

Answer 12

The neural SDF method for geometry rendering can’t use edge sampling for rendering optimisation.

Question 13

Re-param pix int - What are their findings?

Answer 13

That the reparametrisation of the pixel integral gives correct neural SDF differentials.

Question 14

Re-param pix int - What does “differentiable rendering” means?

Answer 14

It means to a differentiable rendering function of a 3D scene in respect to the rendering variables. They are: geometries, materials, light or camera.

Question 15

Re-param pix int - Why is it good to differentiate the rendering? What are possible purposes?

Answer 15

In order to optimise the rendering process for 3D reconstruction, Inverse rendering or Neural Scene representation.

Question 16

Re-param pix int - What is the old way to deal with edges in mesh-based rendering differentiation?

Answer 16

To do edge based sampling meaning to explicitly consider the silhouette in the sampling to build the differentials around the edge.

Question 17

Re-param pix int - Why SDFs do not have a simple parametric form amenable to sampling?

Answer 17

Because they are implicitly define the edge as the area in which SDF(x)=0.

Question 18

Re-param pix int - What is their contribution?

Answer 18

They develop a continuous warping function so neural SDFs rendering can be differentiable.

Question 19

Re-param pix int - How they developed a continuous warping function?

Answer 19

They use the distance to the surface and quadrature on sphere tracer points.

Question 20

Open3D - why should one divide the world to blocks and only then to voxels

Answer 20

Because blocks are a more coarse unit that helps optimise the rendering because of the sparse nature of objects in a scence. Voxels help to add more fine details.

Question 21

Open3D - why we do not maintain a voxel hash map?

Answer 21

we can preserve the data locality instead of scattering adjacent data uniformly into the memory.