Point Cloud Interpolation

Point cloud interpolation aims to reconstruct missing or incomplete 3D point cloud data over time, addressing challenges like sparsity and complex, non-rigid deformations. Recent research heavily utilizes deep learning, particularly 4D neural fields and Gaussian deformation fields, to model spatiotemporal dynamics and generate smooth, continuous interpolations. These methods often incorporate techniques like temporal consistency learning and optimal transport embeddings to improve accuracy and efficiency. Successful applications are demonstrated in diverse areas such as autonomous driving and human motion capture, highlighting the importance of this field for accurate 3D scene reconstruction and analysis.