tamiwiki:projects:scanning-tami
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| tamiwiki:projects:scanning-tami [2025/02/06 19:37] – wissotsky | tamiwiki:projects:scanning-tami [2025/02/24 13:24] (current) – wissotsky | ||
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| <WRAP center round todo 60%> | <WRAP center round todo 60%> | ||
| - | Very WIP page(as of 5th February 2025), you can help by asking questions in the telegram group or in-person on Mondays | + | Very WIP page(as of 24th February 2025), you can help by asking questions in the telegram group or in-person on Mondays |
| </ | </ | ||
| 3d scan of tami | 3d scan of tami | ||
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| + | {{tamiwiki: | ||
| the scan is based on a phone video recording of me walking around tami. | the scan is based on a phone video recording of me walking around tami. | ||
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| After that I switched to superpoint for the feature detection and lightglue for feature matching which seems to be a fairly popular combo currently. | After that I switched to superpoint for the feature detection and lightglue for feature matching which seems to be a fairly popular combo currently. | ||
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| First I matched every frame with its 30 nearest frames(by time). | First I matched every frame with its 30 nearest frames(by time). | ||
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| I was able smooth the depth over by rasterizing the median depth(according to RaDe-GS) | I was able smooth the depth over by rasterizing the median depth(according to RaDe-GS) | ||
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| But I still had some outliers in the depth data. | But I still had some outliers in the depth data. | ||
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| But the problem I got there is that the outliers were visibly clearly still in the depth data but brought into the distribution and blended into it. | But the problem I got there is that the outliers were visibly clearly still in the depth data but brought into the distribution and blended into it. | ||
| - | #TODO | + | After plotting the rasterized median depth from gaussian splats into a frequency histogram I was able to see that in problematic images there are two distinct spikes and a long trail of depths. |
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| + | I was able to fit a kernel density estimate to the depth data and then I manually found cutoff value where if the density after the global peak becomes lower it means that were past the primary peak any depth beyond that is an outlier. | ||
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| + | {{tamiwiki: | ||
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| + | After removing the depth outliers I was able to get much cleaner results | ||
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| + | To get a mesh from the depth images I used TSDF integration, | ||
| - | depth kernel density outlier detection | + | But the gpu vram wasnt enough for me to extract mesh detail down to 1mm. And running the integration purely on cpu was too slow. |
| - | tsdf integration | + | So I ended up computing the tsdf volume in batches on the gpu and them merging them onto a uniform voxel grid on the cpu, where there was overlap between the grids I used trilinear interpolation. |
| - | mesh compresson | + | #TODO mesh compresson |
| voxelization | voxelization | ||
tamiwiki/projects/scanning-tami.1738863440.txt.gz · Last modified: 2025/02/06 19:37 by wissotsky
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