added notes Nov 29 and gitignore for obsidian

docs/Notes/.gitignore

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+.obsidian

docs/Notes/To_Do.md

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+Eva
+- [ ] upsample meshes
+	- [ ] fill holes
+	- [ ] upsample
+	- [ ] clean
+- [ ] double check point numbers pre and post crop to make sure no issues 
+
+Jan
+- [ ] add scaling factor in GUI
+	- in global_registration:
+		- Arthur used: 
+		-  `distance_threshold = voxel_size * 1.5 
+		- voxel_size = size / 55 (size is bounding box)`
+		-  so our default distance_threshold in GUI should be size*1.5/55 
+
+
+	- so to replicate Arthur's settings
+		- if we do 0.007 factor (0.7%) of bounding box size for distance_threshold for ICP
+		- then 0.027 (2.7%) of bounding box size for distance_threshold for Ransac
+		- can interconvert, I think it makes sense for user to put it goal for ICP since that is the final one, and calculate Ransac from that
+
+
+
+*Idea for setting max deformation allowed*
+
+- check after rigid alignment max distance between two corresponding points (on cropped models only)
+	- could use DeCa for this since the models are already aligned
+- then use that to inform lambda value for deformation 
+	- ie model allowed to deform enough to match proximal surfaces but distal surface can only deform by this amount
+
+
+
+*Other notes from Meeting 29/11/24*
+- we discussed that SSMs will not solve our problem (as Jan already stated in thesis defense)
+	- gives variation in length but would not necessarily help predict where within that feasible space the individual falls
+		- indeed, other SSM methods for distal humerus reconstruction have issues with getting length exact
+	- we would be happy to get similar errors to them
+	- but we can do so by limiting deformation as discussed above
+		- true length could only really be predicted if some part of proximal morphology can be used as predictor of distal
+- future idea find closest match in proximal morphology (after scaling and rigid alignment)
+	- check if finding closest match from "libary" of models used to create mean model gives better results than using mean model