High-Res DEMs from single HiRISE images, First results of new "Shape from Shading" algorithm |
High-Res DEMs from single HiRISE images, First results of new "Shape from Shading" algorithm |
Jan 16 2010, 03:30 PM
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Member Group: Members Posts: 713 Joined: 30-March 05 Member No.: 223 |
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. |
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Mar 3 2010, 06:37 PM
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Member Group: Members Posts: 713 Joined: 30-March 05 Member No.: 223 |
For comparison, here is (a crop of) the original, radiometrically calibrated HiRISE-image I used as input for the 3D reconstruction algorithm (all images at half resolution) A:
which yields the following DEM (greylevel = height) B: Now, once I have the DEM I can re-render it under exactly the same illumination conditions (incidence angle, sub-solar azimut etc.) as the original image, which yields this view: C: So comparing A and C gives an idea of the accuracy of the reconstruction process (as can be seen, the so called "photometric error" is almost zero). However, the low photometric error does not tell the whole story because it does not address the albedo variations which can not be resolved from a single image alone without additional information. The comparison shows, that the reconstruction can be quite exact, at least in the absence of albedo variations... (The increased contrast in C is due to the higher dynamic range of greylevels used by the 3D renderer compared to the somewhat hazy original image) |
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