High-Res DEMs from single HiRISE images, First results of new "Shape from Shading" algorithm Options

[Nirgal]

Hi all,

Here the long overdue continuation of the "Alien Landscapes" series. This time based on 3D
DEMs generated with "Shape from Shading" from single HiRISE images.
Enjoy

Click on Images for larger version.

Detail views from PSP_002172_1410 (large gully system)





Detail view of Gullies from PSP_001376_1675





Detail of gully system in PSP_002022_1455



Dune Views from PSP_004339_1890





Detail from PSP_001834_1605





Here is some background info on the making of the images:

"Shape from Shading" (SFS) i.e. the possibility to extract shape information from a single image has always been a fascinating topic for me.
Now I found the time to implement a prototype for a new SFS algorithm based on some ideas that I've been thinking about for a long time.
The problem with existing SFS approaches (see here for a survey is that they either tend to over-smooth the details (due to the regularization constraint) or suffer from excessive noise in the high-frequency components of the reconstructed surface. Another problem is the large demand on CPU ressources which would make them very challenging to apply to large scale input data, such as HiRISE orbiter images.

So for a long time I was rather sceptical as to the potential of SFS and it was my impression that Methods based on multiple images (stereo) must be far superior to single-image SFS.

However, after a long time of experimenting, combining existing approaches with some new ideas, I got the following quite promising first results that I'd like to share:

All of the images were generated from a single HiRISE image (no depth information was used from stereo or laser altimeter data).
Also, no texturing or additional coloring/shading was applied when rendering the surface.
Every detail visible is real 3D down to the pixel-level...
For rendering I used a very simple model based on lambertian reflection with gouraud shading.

The resolution of the images is still moderate: that is downsampled details crops in the order of 0.5-1 Megapixels.
However, despite the heavy math machinery that drives the core of the algoritm (several systems of equations with millions of unknowns)
the processing time is still moderate (about 15 Minutes per med-res image, using about 2 Gigs main mem) such that the application to full-res HiRISE images should be possible

The following image shows an example to illustrate the general principle (click to enlarge).


On the left hand side the 2D input image (simple noisy JPEG from the Web with unknwon light source direction). On the right hand side shows the recovered 3D surface re-lighted under a different light source direction.
Note that one problem of the current implementation of the algorithm is it's vulnerability to notable distortions in the low frequency components (i.e. large scale variations) of the generated surface. However I'm confident that this can be overcome by an improved version or by adding the large-scale depth information from stereo-based DEMs or altimeter data (MOLA) where available.

[mcaplinger]

Not to be dismissive of this, but as Nirgal pointed out himself, SFS/photoclinometry has been used in the planetary science community for a long time but just isn't very accurate in a lot of cases. While it may be sufficient for pretty visualizations, do you have any sense for how truly accurate your algorithm is?

[Nirgal]

Not to be dismissive of this, but as Nirgal pointed out himself, SFS/photoclinometry has been used in the planetary science community for a long time but just isn't very accurate in a lot of cases. While it may be sufficient for pretty visualizations, do you have any sense for how truly accurate your algorithm is?

That's right: the main application of the algorithm (at least in its current form) is clearly the area of plausible visualization in near-photorealistic image resolution. (and that's the application that I'm personally most interested in)
In this area it seems to be a very interesting tool to visually explore and display the huge space imaging datasets, particularly when there is no other 3D cue like altimeter or stereo data available.
The question of actual physical accuracy however, is much more difficult to answer.
In this regard, there are clear limitations to any shape-from-single-image methodology as already pointed out.
This is where the already mentioned combination with low-res but more accurate data (altimeter, stereo) comes into play ...
So in summary: yes, this project (in its current state) is intended primarily as a means of visualization, not so much as a scientific measuring instrument (of which there are already many other and better possibilities).

For me, it has been an old childhood dream to literally "wander into" and "bring to live" the images that were sent back by our space probes ... images to fuel the imagination of space exploration