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On this page
  • CasualStereo
  • Volume rendering
  • Physically based rendering
  • Image based rendering
  • Plentopic function
  • Methods
  • Quicktime VR
  • Layered depth images
  • Light fields
  • Lumigraph
  • Multiplane images
  • Casual real-world light fields
  • Immersive light fields
  • Outlook NERFS

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  1. Computer Graphics and Vision

Rendering

The essence of computer graphics

We have all the stereo, 3D reconstruction and mosaicing data but what to do with them?

One example is real-time graphics

  • VR headset

  • Augmented reality

  • Video games

  • Visualisations

CasualStereo

  • Same method as a panorama image, but creating stereo panoramas

  • Handheld capture (casual)

  • Structure from motion to determine camera poses

  • Able to handle spherical motion (i.e. arms stretched out to keep motion on surface of a sphere)

  • View syntehsis to generate perfect circle of images

  • Extract left and right eye columns to compute two panoramas

They wanted a method that was quick and easy to use.

Volume rendering

Made up of voxels, so a solid 'object' of data

  • Simulations (fluid dynamics)

  • Geoscience data

  • Medical imaging data

  • Material science data

Types

  • Slicing - simply take pixel values for some slice cross section

  • Indirect - make a mesh out of it

    • Contour lines

    • Marching cubes

  • Direct

    • Image based

      • Ray tracing

      • Colour value from each voxel

      • Weighting from transparency values

    • Object based

      • Each voxel to calculate colour in image (splatting)

    • Texture-based

      • Texture mapping in GPU

Ray tracing

  • Cast ray into scence, sample along ray.

    • e.g. grayscale take the highest intensity as the value

Physically based rendering

Phong

  • Ambient + diffuse + specular

Global illumination

  • Direct + indirect lighting

  • Shadows

  • Inter-object reflections

  • Radiosity

BRDF (Bidrectional Reflectance Distribution Function)

  • Ratio of light coming from one direction that gets reflected in another direction

  • Pure reflection, assumes no light scatters

  • Focuses on angular aspects, not spatial variation

  • Can be measured from real objects

  • Cook-Torrance BRDF includes components of scattering and mirroring

    • Takes into account microfacets in the surface (how rough it is)

Image based rendering

Given images and geometry

  • Produce new images

Benefits

  • No labour to model

  • Captures high complexity

  • Rendering time depends only on image size not complexity

Plentopic function

Or a 'light field' describes light flowing in every direction through every point in space. A 5D function to map this.

Methods

Quicktime VR

  • Stitch panoramas together, let you explore them in 360 view

Layered depth images

  • Similar to a sprite with depth, pixels contain depth values and colours

  • Some depths may be occluded

Use by facebook for the one shot 3D stuff.

  • Take an image

  • estimate depth

  • generate layers

  • colour inpainting

  • mesh

  • novel view

Light fields

  • 4D slice of plenoptic function (fixed time)

  • Assumes free of occluders

  • Parameterize rays by intersection with two planes

I.e. take a bunch of images and can then sample new views

  • Rotation of camera allowed with a rotating object to get a bunch of views

Lumigraph

  • Unstructured capture

  • Precalibrated camera and camera poses computed by markers

  • Rough geometric model of object

  • Resamples images to two plane representation

Multiplane images

  • Approximate light field as a stack of semi-transparent, coloured layers arranged at various depths

  • Uses gradient descent to generate an MPI from a set of sparse camera viewpoints

  • Training datra based on a set of input views and a crop within target view

  • Best optimized for forward-looking views from position near center of projection

Casual real-world light fields

  • Compute camera poses using custom structure-from-motion technique

  • Predict an MPI for each sampled image with a 3D CNN

  • Compute depth maps from predicted MPIs

  • MPIs render views by re-projecting its layers into the novel view and compositing them from back-to-front

Immersive light fields

  • 46 low cost action cameras, all on a hemisphere

  • Uses multi-sphere images instead of MPI to provide panoramic experiences as needed for VR

Outlook NERFS

  • Neural volumes (consiting of opacity and colour at each 3D point)

  • Neural Radiance Fields (Uses DeepSDF but density and color + numerical integration to approximate volumetric rendering)

Really good, keep fine details and are temporally stable unlike other methods

PreviousStereo VisionNextExam papers

Last updated 3 years ago

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