24 research outputs found
PointGrow: Autoregressively Learned Point Cloud Generation with Self-Attention
A point cloud is an agile 3D representation, efficiently modeling an object's
surface geometry. However, these surface-centric properties also pose
challenges on designing tools to recognize and synthesize point clouds. This
work presents a novel autoregressive model, PointGrow, which generates
realistic point cloud samples from scratch or conditioned on given semantic
contexts. Our model operates recurrently, with each point sampled according to
a conditional distribution given its previously-generated points. Since point
cloud object shapes are typically encoded by long-range interpoint
dependencies, we augment our model with dedicated self-attention modules to
capture these relations. Extensive evaluation demonstrates that PointGrow
achieves satisfying performance on both unconditional and conditional point
cloud generation tasks, with respect to fidelity, diversity and semantic
preservation. Further, conditional PointGrow learns a smooth manifold of given
image conditions where 3D shape interpolation and arithmetic calculation can be
performed inside
Iterative SE(3)-Transformers
When manipulating three-dimensional data, it is possible to ensure that
rotational and translational symmetries are respected by applying so-called
SE(3)-equivariant models. Protein structure prediction is a prominent example
of a task which displays these symmetries. Recent work in this area has
successfully made use of an SE(3)-equivariant model, applying an iterative
SE(3)-equivariant attention mechanism. Motivated by this application, we
implement an iterative version of the SE(3)-Transformer, an SE(3)-equivariant
attention-based model for graph data. We address the additional complications
which arise when applying the SE(3)-Transformer in an iterative fashion,
compare the iterative and single-pass versions on a toy problem, and consider
why an iterative model may be beneficial in some problem settings. We make the
code for our implementation available to the community