×
Well done. You've clicked the tower. This would actually achieve something if you had logged in first. Use the key for that. The name takes you home. This is where all the applicables sit. And you can't apply any changes to my site unless you are logged in.

Our policy is best summarized as "we don't care about _you_, we care about _them_", no emails, so no forgetting your password. You have no rights. It's like you don't even exist. If you publish material, I reserve the right to remove it, or use it myself.

Don't impersonate. Don't name someone involuntarily. You can lose everything if you cross the line, and no, I won't cancel your automatic payments first, so you'll have to do it the hard way. See how serious this sounds? That's how serious you're meant to take these.

×
Register


Required. 150 characters or fewer. Letters, digits and @/./+/-/_ only.
  • Your password can’t be too similar to your other personal information.
  • Your password must contain at least 8 characters.
  • Your password can’t be a commonly used password.
  • Your password can’t be entirely numeric.
Enter the same password as before, for verification.
Login

Grow A Dic
Define A Word
Make Space
Set Task
Mark Post
Apply Votestyle
Create Votes
(From: saved spaces)
Exclude Votes
Apply Dic
Exclude Dic

Leveraging SE(3) Equivariance for Learning 3D Geometric Shape Assembly. (arXiv:2309.06810v1 [cs.CV])

Click here to flash read.

Shape assembly aims to reassemble parts (or fragments) into a complete
object, which is a common task in our daily life. Different from the semantic
part assembly (e.g., assembling a chair's semantic parts like legs into a whole
chair), geometric part assembly (e.g., assembling bowl fragments into a
complete bowl) is an emerging task in computer vision and robotics. Instead of
semantic information, this task focuses on geometric information of parts. As
the both geometric and pose space of fractured parts are exceptionally large,
shape pose disentanglement of part representations is beneficial to geometric
shape assembly. In our paper, we propose to leverage SE(3) equivariance for
such shape pose disentanglement. Moreover, while previous works in vision and
robotics only consider SE(3) equivariance for the representations of single
objects, we move a step forward and propose leveraging SE(3) equivariance for
representations considering multi-part correlations, which further boosts the
performance of the multi-part assembly. Experiments demonstrate the
significance of SE(3) equivariance and our proposed method for geometric shape
assembly. Project page: https://crtie.github.io/SE-3-part-assembly/

Click here to read this post out
ID: 398293; Unique Viewers: 0
Unique Voters: 0
Total Votes: 0
Votes:
Latest Change: Sept. 14, 2023, 7:32 a.m. Changes:
/u/anonymous
Dictionaries:
Words:
Spaces:
Views: 11
CC:
No creative common's license
Comments: