@bearprin yes, you're right. The mean is essentially a normalization by number of points in the subsamples. Looks like i forgot it in the code. Also note that the shapes are asumed to be normalized to unit cube size to get comparable results. #21
@bearprin yes, you're right. The mean is essentially a normalization by number of points in the subsamples. Looks like i forgot it in the code. Also note that the shapes are asumed to be normalized to unit cube size to get comparable results.
If the number of points in the subsamples is always the same, the error will be off by a constant factor (10000) here. Because of the default optimization in trimesh's surface sampling, which rejects too close samples, the correct CD might be ~5% lower than stated in the paper. However, this should apply to all methods very similarly. I'm sorry for this inaccuracy. A PR would be very welcome.
@bearprin yes, you're right. The mean is essentially a normalization by number of points in the subsamples. Looks like i forgot it in the code. Also note that the shapes are asumed to be normalized to unit cube size to get comparable results.
If the number of points in the subsamples is always the same, the error will be off by a constant factor (10000) here. Because of the default optimization in trimesh's surface sampling, which rejects too close samples, the correct CD might be ~5% lower than stated in the paper. However, this should apply to all methods very similarly. I'm sorry for this inaccuracy. A PR would be very welcome.
Originally posted by @ErlerPhilipp in https://github.com/ErlerPhilipp/points2surf/issues/20#issuecomment-1138394995