In our comments we address the reviews in the order of the reviewer number. The reviewer notes that on page 4 line 152, the phrase "no limitation is needed on the receptive field size" would benefit DGCNN than to PointNet as it also uses graph convolutions. The FoldingNet decoder alone has 1.05M parameters. Unfortunately the parameters count of the encoder are not stated, but the code indicates around 0.65M parameters The reviewer points out that some additional ablation study might be beneficial.

Training deep learning models for point cloud prediction tasks such as shape completion and generation depends critically on loss functions that measure discrepancies between predicted and ground-truth point sets. Commonly used functions such as Chamfer Distance (CD), HyperCD, and InfoCD rely on nearest-neighbor assignments, which often induce many-to-one correspondences, leading to point congestion in dense regions and poor coverage in sparse regions. These losses also involve non-differentiable operations due to index selection, which may affect gradient-based optimization. Earth Mover Distance (EMD) enforces one-to-one correspondences and captures structural similarity more effectively, but its cubic computational complexity limits its practical use. We propose the Adaptive Probabilistic Matching Loss (APML), a fully differentiable approximation of one-to-one matching that leverages Sinkhorn iterations on a temperature-scaled similarity matrix derived from pairwise distances. We analytically compute the temperature to guarantee a minimum assignment probability, eliminating manual tuning. APML achieves near-quadratic runtime, comparable to Chamfer-based losses, and avoids non-differentiable operations. When integrated into state-of-the-art architectures (PoinTr, PCN, FoldingNet) on ShapeNet benchmarks and on a spatiotemporal Transformer (CSI2PC) that generates 3D human point clouds from WiFi CSI measurements, APM loss yields faster convergence, superior spatial distribution, especially in low-density regions, and improved or on-par quantitative performance without additional hyperparameter search. The code is available at: https://github.com/apm-loss/apml.

Digital dentistry has made significant advancements in recent years, yet numerous challenges remain to be addressed. In this study, we release a new extensive dataset of tooth meshes to encourage further research. Additionally, we propose Variational FoldingNet (VF-Net), which extends FoldingNet to enable probabilistic learning of point cloud representations. A key challenge in existing latent variable models for point clouds is the lack of a 1-to-1 mapping between input points and output points. Instead, they must rely on optimizing Chamfer distances, a metric that does not have a normalized distributional counterpart, preventing its usage in probabilistic models. We demonstrate that explicit minimization of Chamfer distances can be replaced by a suitable encoder, which allows us to increase computational efficiency while simplifying the probabilistic extension. Our experimental findings present empirical evidence demonstrating the superior performance of VF-Net over existing models in terms of dental scan reconstruction and extrapolation. Additionally, our investigation highlights the robustness of VF-Net's latent representations. These results underscore the promising prospects of VF-Net as an effective and reliable method for point cloud reconstruction and analysis.