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--- tamiwiki:projects:scanning-tami [2025/02/03 20:28] – created wissotsky
+++ tamiwiki:projects:scanning-tami [2025/02/24 13:24] (current) – wissotsky
@@ Line 1: / Line 1: @@
+<WRAP center round todo 60%>
+Very WIP page(as of 24th February 2025), you can help by asking questions in the telegram group or in-person on Mondays
+</WRAP>
 d scan of tami
-camera registration (superpoint+lightglue)
+{{tamiwiki:projects:he-is-speaking-guy-explaining-with-a-whiteboard.gif}}
+the scan is based on a phone video recording of me walking around tami.
+Now to get from a video to a mesh/gaussian splat the first step is to figure out where all the frames are relative to each other in space.
+(aka Global Registration)
+The first two steps consist of feature detection and matching.
+The rolling shutter and overexposure from the video were too complex for the SIFT based colmap feature detection.
+Given that we have 3014 frames from the video, the exhaustive matching (3014**2 matches) from colmap is way too slow.
+We can make assumptions about our data, such as the fact that time moves forward and that our camera moves linearly in space(aka no teleportation)
+to reduce the amount of matches we have to do.
+At first I tried using RoMa which is the current SOTA model combining feature detection and matching(https://arxiv.org/abs/2305.15404v2). But it was too slow per each pair.
+After that I switched to superpoint for the feature detection and lightglue for feature matching which seems to be a fairly popular combo currently.
+{{tamiwiki:projects:animation_tamiscan_superpoint_lightglue.gif}}
+First I matched every frame with its 30 nearest frames(by time).
+Then I manually found cases where I revisit the same place in the video and matched between such frames to serve as loop closure points.
+After converting the point data into a colmap database, I used colmap's transitive pair generator to complete the matching.
+Then used GLOMAP on the image pairs to get the global poses and a sparse point cloud.
+This scene can already be used for 3d gaussian splatting, but it had alot of issues recovering fine details anywhere outside the initial sparse point cloud, random initialization helped but then it struggled with big floaters and incredibly long training times.
+I tried other 3dgs pruning schedules/techniques such as AbsGS and PixelGS but they had overall worse results as their repos didnt include depth regularization and antialiasing.
+I decided to try making a dense point cloud, but the colmap mvs was way too slow to compute.
+I tried projecting points from a monocular depth estimate(apple depth pro) but it was too globally unstable between frames.
+Fortunately OpenMVS's patchmatch implementation was much faster and had more flexible parameters compared to colmap
+I was able to run a quick low resolution depth estimate that I then used as ground truth for realigning a neural monocular depth estimate.
+OpenMVS .dmap files include a depth map and a confidence map, I was able to use this with RANSAC to fit a polynomial which offset the neural depth map
+The confidence map was used to change the contribution of samples and allowed me to fit even very hard cases.
+Now I was able to reproject all the realigned depths into a very dense pointcloud for the scene. Much denser that would even be possible with patchmatch
+Albeit this point cloud had visible layers because of small discrepancies between the depths and views
+Fortunately 3dgs is specifically good at optimizing these kinds of cases into a multiview consistent scene.
+After running 3dgs the results were quite good, and I decided to move onto creating a mesh that could be used in software like blender.
+Trying a bunch of papers, the best results I got were from ones using TSDF integration for their meshing workflow, but none of them had all the features that I wanted so I decided to do it myself.
+After rendering depth maps from 3dgs and integrating them into a tsdf volume I saw that there were alot of "spikes" and outliers in the depth(due to just splat ordering)
+I was able smooth the depth over by rasterizing the median depth(according to RaDe-GS)
+{{tamiwiki:projects:combined_depths_animation.gif}}
+But I still had some outliers in the depth data.
+I tried again fitting neural depth estimates to my 3dgs depth data, but at this point their level of detail was lower than what I was getting from 3dgs.
+I then tried PromptDA which is meant to upscale depths from smarthpones and lidars using a low-rez depth + rgb image pairs -> hi-rez depth.
+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.
-coarse patchmatch mvs depth estimation
+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.
-alignment of neural monocular estimate to patchmatch(ransac polynomial fitting)
+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.
-dense point cloud integration
+{{tamiwiki:projects:threshold_animation.gif}}
-dgs scene refinement
+After removing the depth outliers I was able to get much cleaner results
-dgs depth rasterization
+To get a mesh from the depth images I used TSDF integration, I used the VoxelBlockGrid implementation from open3d.
-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

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