[HN Gopher] N-Dimensional Gaussians for Fitting of High Dimensio...
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       N-Dimensional Gaussians for Fitting of High Dimensional Functions
        
       Author : jasondavies
       Score  : 56 points
       Date   : 2024-05-17 12:18 UTC (10 hours ago)
        
 (HTM) web link (www.sdiolatz.info)
 (TXT) w3m dump (www.sdiolatz.info)
        
       | 3abiton wrote:
       | This problem specifically (3D reconstruction with representation
       | fitting) is really an overfitting nightmare, they just adapted to
       | it not really overcame it. Nonetheless interesting work.
        
         | blovescoffee wrote:
         | The point of a Nerf is effectively overfitting a neural network
         | to a scene anyways (I think they say this in the original NeRF
         | paper - or somewhere similar).
        
       | vessenes wrote:
       | When I think of turning sequences of images into gaussians, I
       | think of the difficulty of getting generalizable information that
       | can be re-rendered out of the pipeline; textures and lighting,
       | basically. From the description at the top of the paper, where
       | they mention adding dimensions for things like albedo, I got
       | excited.
       | 
       | But the demos don't do any re-rendering / change of lighting /
       | etc, so I can't tell if this paper is just a 'super high render
       | quality at same training time' paper, which is of course great to
       | have, or if it has a shot at being extended to get us scenes that
       | can be adjusted as to lighting and texture in-engine.
       | 
       | Any experts care to chime in?
        
         | WithinReason wrote:
         | The penultimate video has a lighting change
        
       | hackandthink wrote:
       | I expected Gaussian processes.
       | 
       | Can someone relate this to Gaussian processes?
        
         | blovescoffee wrote:
         | If you understand what a Gaussian Process is, you understand
         | what a Gaussian Distribution is. This work estimates the
         | parameters of many Gaussian Distribution in order to fit the
         | approximate geometry of a GD to a geometry in a scene.
        
         | siddboots wrote:
         | A Gaussian process fits a single high dimensional Gaussian, for
         | example, by treating n observations along a single dimension as
         | a n dimensional space.
         | 
         | Gaussian mixture models fit a large number of low dimensional
         | Gaussians for example you might imagine 2D data generated by
         | several 2D Gaussian superimposed.
         | 
         | This approach is just an example of the latter. It uses higher
         | dimensional Gaussians to capture extra information from a
         | scene, but not in the emulation of an infinite dimensional
         | space in the way that defines Gaussian processes.
        
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       (page generated 2024-05-17 23:01 UTC)