Intrinsic Point Cloud Interpolation via Dual Latent Space Navigation
This addresses shape manipulation for 3D computer vision applications, but appears incremental as it builds on existing learning-based interpolation methods.
The paper tackles the problem of interpolating 3D point clouds while preserving intrinsic shape properties, resulting in more realistic and smoother interpolations compared to baselines.
We present a learning-based method for interpolating and manipulating 3D shapes represented as point clouds, that is explicitly designed to preserve intrinsic shape properties. Our approach is based on constructing a dual encoding space that enables shape synthesis and, at the same time, provides links to the intrinsic shape information, which is typically not available on point cloud data. Our method works in a single pass and avoids expensive optimization, employed by existing techniques. Furthermore, the strong regularization provided by our dual latent space approach also helps to improve shape recovery in challenging settings from noisy point clouds across different datasets. Extensive experiments show that our method results in more realistic and smoother interpolations compared to baselines.