Terrain Objects

In this YouTube Video:

I told potential viewers, that there can be more than one way, in which a 2D Image can be transformed into some form of 3D Geometry, in the form of a mesh, suitable for CGI. This took the form of Terrain Objects.

Some of my readers may already know a lot about Terrain Objects, but then again, some may not.

There was a detail to Terrain Objects which my screen-cast failed to explain. Given a serious game engine, or other 3D rendering engine, this will offer its content-developer a variety of different objects, which he or she can build a game, etc., out of. And most game engines, will actually implement Terrain Objects as being different Entities, from generic Models. Not only that, but Convex Models exist in addition to the types of Models, that would be used to represent Actors… And the exact way in which this is organized usually depends on the game engine.

What most game engines will do is actually allow their content-developer just to specify a height-map, which refers to the 2D image the pixel-values of which are the heights, and to convert this into a 3D mesh behind the scenes for him. Not only that, but powerful game engines will actually support ‘Chunked Terrain’ in some way, which means that the Terrain of a given Game Level is subdivided into Chunks, not all of which need to be loaded onto the graphics card at once.

The reason fw this is done, is the fact that the actual 3D mesh consumes far more graphics memory, than a 2D Image would, especially in the case of Terrains. Not having to load the geographical definition of an entire world at once, has its benefits.

But I also felt that it was beyond the scope of my video, to explain that.

(Update 05/08/2018, 15h35 … )

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A Method for Obtaining Signatures for Photogrammetry

I have posed myself the question in the past, in which a number of photos is each subdivided into a grid of rectangles, how a signature can be derived from each rectangle, which leads to some sort of identifier, so that between photos, these identifiers will either match or not, even though there are inherent mismatches in the photos, to decide whether a rectangle in one photo corresponds to the same subject-feature, as a different rectangle in the other photo.

Eventually, one would want this information in order to compute a 3D scene-description – a 3D Mesh, with a level of detail equal to how finely the photos were subdivided into rectangles.

Since exact pixels will not be equal, I have thought of somewhat improbable schemes in the past, of just how to compute such a signature. These schemes once went so far, as first to compute a 2D Fourier Transform of each rectangle @ 1 coefficient /octave, to quantize those into 1s and 0s, to ignore the F=(0,0) bit, and then to hash the results.

But just recently I have come to the conclusion that a much simpler method should work.

At full resolution, the photos can be analyzed as though they formed a single image, in the ways already established for computing an 8-bit color palette, i.e. a 256-color palette, like the palettes once used in GIF Images, and for other images that only had 8-bit colors.

The index-number of this palette can be used as an identifier.

After the palette has been established, each rectangle of each photo can be assigned an index number, depending on which color of the palette it best matches. It would be important that this assignment not take place, as though we were just averaging the colors of each rectangle. Instead, the strongest basis of this assignment would need to be, how many pixels in the rectangle match one color in the palette. (*)

After that, each rectangle will be associated with this identifier, and for each one the most important result will become, at what distances from its camera-position the greatest number of other cameras confirm its 3D position, according to matching identifiers.


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