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1. Stable View Synthesis

2. What is View Synthesis?
● View synthesis involves creating a 3D view from a series of 2D images.
● This can be done using a series of photos that show an object from multiple
angles, create a hemispheric plan of the object, and place each image in the
appropriate place around the object.
● A view synthesis function attempts to predict the depth given a series of
images that describe different perspectives of an object.

3. What is Volume Rendering
● creates an image synthesized from multiplane images (MLP)
● obtains the RGB for every voxels in the space through which rays from the
camera are casted

4. Input and Output
Its input is a 3D location x = (x; y; z) and 2D viewing direction (θ; Φ)
Its output is an emitted color c = (r; g; b) and volume density (α).

5. What is Stable View Synthesis?
The method operates on a geometric scaffold computed via structure-from-motion
and multi-view stereo.
trained end-to-end. It supports spatially-varying view-dependent importance
weighting and feature transformation of source images at each point; spatial and
temporal stability due to the smooth dependence of on-surface feature
aggregation on the target view; and synthesis of view-dependent effects such as
specular reflection.
outperforms state-of-the-art view synthesis
More “temporally stable” (meaning consistent per frames)

6. constructs a 3D geometric scaffold via structure from-motion, multi-view stereo, ,
and meshing.
convolutional network is trained to map “deep features” onto geometric scaffold.
In Scene Representation Networks, the volume is represented as an MLP and
images are rendered via differentiable ray marching.

7. What is NeRF++
Neural Radiance Fields
● a technique that generates 3D
representations of an object or scene
from 2D images by using advanced
machine learning
● volume rendering algorithm obtains
the RGB for every voxels in the space
through which rays from the camera
are casted
● represents scenes as continuous
functions, enabling the generation of
novel views
SVG

8. What is FVS
Free View Synthesis
Useful for generating unlimited viewing
angles
More demanding than stable view
Less “temporally coherent”
SVS has up to 10% less errors
SVG

9. What is NPBG
Neural Point Based Graphics
Uses Deep Learning models to predict
pixel values at certain points
Represent scenes using volumetric “point
clouds”, which are like voxels in 3D space
NPBR can scale well and understand
lighting because of its ability to generalize
complex scenes using AI.
Some black artifacting present in this
approach
SVG

10. Structure from
Motion
Multi view Stereo
Surface
Reconstruction

11. Convolutional
Network

19. Sources
https://arxiv.org/abs/2011.07233
https://datagen.tech/guides/synthetic-data/neural-radiance-field-nerf/
https://paperswithcode.com/paper/free-view-synthesis
https://youtu.be/gqgXIY09htI?si=f4cgJcSNllkEm2-q
https://youtu.be/CZW6HLDQRD4?si=btumM36-3O5jN2pF