Edge fitting

[ZhouCX117]

Thanks for your excellent! How could I use the EMAP code to fit curves and lines from the trained 3DGS? Now I regard the Gaussian centers and directions as points and linedirection and feed them into the get_parametric_edge function of EMAP https://github.com/cvg/EMAP/blob/main/src/edge_extraction/extract_parametric_edge.py. Is it right?

[kunalchelani]

Hello! Yes, that would be one way to do it. However, EMAP's method of clustering of points with direction, and also ours is fitted to the distribution of points and associated direction (and noise in it) obtained from the underlying process (voxel-based UDF and Gaussians respectively). So, if you haven't applied the geometric regularization to the Gaussians mentioned in our paper, you would get a slightly different distribution of Gaussian centers. It is however worth trying out the steps you mention to establish one baseline. Curious to know how it goes!

[ZhouCX117]

I have applied the geometric regularization to the initial Gaussians and utilized the EMAP's code to fit curves and lines. The results are bad. It seems the trained Gaussians are noisy. Could you please provide the code described in Section 3.3?

[kunalchelani]

We will be releasing the code soon. Thanks for the patience!

[Jiahao-Ma]

Thanks for your awesome work. I am still curious about edge filtering. Some Gaussians do not lie on the edge. Should we first filter and select only those Gaussians on the edge before applying EMAP's method to extract the edge, or should we apply EMAP's method to all Gaussians?

[kunalchelani]

Filtering is indeed helpful if there are wayward Gaussians. You can also fit edges to all Gaussians and then filter out edges that do not project to edges in the 2D edge maps, how it was done in EMAP. In our work, we performed opacity based filtering, along with filtering statistical outliers. For ABC-NEF objects, we also filtered Gaussians not projecting to an edge in at least a minimum percentage (5-10%) of total views.