Yesterday: (last month) Not dead.
- Been looking into how to save vertices based on what is visible - still struggling with that, though I have a few ideas, and may just sit down one evening with a movie and workout the poor approach of doing this.
- TA responsibilities, including marking
- Struggle, but seems to have come out alright the other end, with articulating the problem to the supervisor. Summary is that we're working with a variant of the maximal clique problem. The approach I'm taking aims to simplify the solving so its not as extensive. Regardless of which approach taken to identify the cliques, we still need to do some post-processing on the resulting cases: where distances were (briefly? sometimes continuously) maintained and there is overlap, or where a common edge is shared.
For example, for 49 polyhedrons bouncing on a table, we have a number of points that should be distinct, but exhibit overlap with other vertices, and therefore have unique clusters (myPhysicsReduced). Unclear if this is due to the large number of objects, or because within the example, many objects bounce with a common pattern.Today: CG supervisor meeting, including two core take-away implementations to work on: 1. what happens when we define our adjacency graph based on consistency in edge length history (see image above); and 2. what cases need to be considered for object labeling (ie. pattern overlap recognition, ala clique-finding). Given the implementation for 1, the current bottleneck may be related to loading the data (eg. +10,000 points).
Roadblocks: N/A
Where Does this Fit In: Working out accurate and fast segmentation is an essential component of the project. Identifying statistical/weighted graph, and how this will be represented is important for future stages which have noise and points that go in/out of a scene.