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