PRNet: Self-Supervised Learning for Partial-to-Partial...
Transcript of PRNet: Self-Supervised Learning for Partial-to-Partial...
PRNet: Self-Supervised Learning for Partial-to-Partial RegistrationYue Wang Justin M. Solomon
Massachusetts Institute of Technology{yuewangx, jsolomon}@mit.edu
MotivationEmpowering Iterative Closest Point with self-supervised Learning.
Task: RegistrationGiven
find the optimal to align them.
Partial Registration NetworkA general framework for partial-to-partial registration tasks.Analogously to ICP, PRNet is designed to be iterative. The basic steps are as follows,
Iterative/Deep Closest PointIf the correspondences are known, we can define the energy function (1), centroids of and (2), and cross-covariance matrix (3).
(1)
(3)(2)
(4)
What if correspondences are unknown? ICP! It alternates between (5) and (6),
(5)
(6)
If we use the singular value decomposition (SVD) to decompose , then, the alignment is given in closed-form by (4).
Results
In contrast, Deep Closest Point using neural networks to parameterize mapping function as
(7)
(a) Network architecture for PRNet. (b) ACP.
Keypoint Detection. We observe that use L2 norm of features tend to indicate importance of a point.
Gumbel-Softmax. To obtain sharp correspondence, we use Gumbel-Softmax as follows,
(8)
(9)
Examples of scans from The Stanford Scanning Repository
Keypoint detection results
Correspondences for pairs of objects
CodePaperGDP group