are you talking about actual images? Or geometry? Or Images of geometry?
visually comparing things is hard - geometrically comparing them is also hard.
I think generally, tessellating stuff and then comparing the vert positions and numbers is a good starting point if you have model data and lots of time and you’re pretty sure your tessellator will always return the same results for the same input.
If you care about visual similarity then you can compare pixel by pixel with some fuzz factor - probably some ML similarity metrics for images is also an OK approach for this … myself - I would prefer doing histogram comparison or something that I can actually debug.
maybe you’ll find this interesting -
Dynamo/HelixImageComparisonTests.cs at master · DynamoDS/Dynamo (github.com)
we do image comparisons of dynamo’s 3d background preview as part of our test harness - it requires a fuzz factor, and it’s not great - it misses small changes, and is very sensitive to zoom level of the image - but it catches large regressions.