Variational Relational Point Completion Network (2104.10154v1)

Abstract: Real-scanned point clouds are often incomplete due to viewpoint, occlusion, and noise. Existing point cloud completion methods tend to generate global shape skeletons and hence lack fine local details. Furthermore, they mostly learn a deterministic partial-to-complete mapping, but overlook structural relations in man-made objects. To tackle these challenges, this paper proposes a variational framework, Variational Relational point Completion network (VRCNet) with two appealing properties: 1) Probabilistic Modeling. In particular, we propose a dual-path architecture to enable principled probabilistic modeling across partial and complete clouds. One path consumes complete point clouds for reconstruction by learning a point VAE. The other path generates complete shapes for partial point clouds, whose embedded distribution is guided by distribution obtained from the reconstruction path during training. 2) Relational Enhancement. Specifically, we carefully design point self-attention kernel and point selective kernel module to exploit relational point features, which refines local shape details conditioned on the coarse completion. In addition, we contribute a multi-view partial point cloud dataset (MVP dataset) containing over 100,000 high-quality scans, which renders partial 3D shapes from 26 uniformly distributed camera poses for each 3D CAD model. Extensive experiments demonstrate that VRCNet outperforms state-of-theart methods on all standard point cloud completion benchmarks. Notably, VRCNet shows great generalizability and robustness on real-world point cloud scans.

• The paper introduces VRCNet, a novel 3D point cloud completion network employing a dual-stage framework with probabilistic modeling and relational enhancement for fine detail recovery and structural relation encoding.
• VRCNet uses probabilistic modeling via a dual-path VAE and a Relational Enhancement network (RENet) with attention and selective kernels to refine coarse completions using multi-scale local point features.
• Empirical evaluations show VRCNet outperforms existing methods on standard benchmarks and real-world datasets, complemented by the contribution of the large multi-view MVP dataset for training.

Variational Relational Point Completion Network

The advent of the Variational Relational Point Completion Network (VRCNet) marks a noteworthy advancement in the domain of 3D point cloud completion, addressing the prevalent issue of incomplete data emanating from real-world scans. The novelty of VRCNet lies in its dual-stage framework comprising probabilistic modeling and relational enhancement, which collectively surmount the challenges of fine detail recovery and structural relation encoding.

Core Components and Methodologies

Probabilistic Modeling: VRCNet introduces a dual-path architecture facilitating principled probabilistic modeling. This structure captures the latent distributions of both partial and complete point clouds, cementing its foundation in variational auto-encoders (VAEs). The completion path, steered by the reconstruction path during training, enhances the capability of predicting coarse completions, thus providing adaptive 3D anchor points crucial for subsequent relational feature exploitation.

Relational Enhancement: The Relational Enhancement network (RENet) integrates self-attention and selective kernel modules to discern relational point features effectively. This segment of VRCNet finely maneuvers through local shape details and structural symmetries, ensuring high fidelity in the reconstruction process. By leveraging multi-scale local point features, the network refines the generated coarse completions into intricately detailed final outputs.

Empirical Evaluation and Dataset Contribution

VRCNet's prowess is exhibited through extensive experimentation across standard benchmarks and real-world datasets. It consistently outperforms existing methods, showcasing robustness and generalizability on diverse point cloud scans from environments such as KITTI and ScanNet. Notably, the creation of the multi-view partial point cloud dataset (MVP dataset), comprised of over 100,000 scans, enriches the training process by bolstering data diversity and completeness with high-resolution partial shapes captured from uniform camera viewpoints.

Implications and Future Directions

Practically, VRCNet promises enhancements in vision tasks reliant on 3D data, spanning robotics and augmented reality applications where point cloud intricacies define operational success. Theoretically, the integration of variational approaches and relational modeling sets a precedent for further exploration into generative models and adaptive feature representations.

Anticipated future inquiries may delve into the augmentation of VRCNet through adversarial learning paradigms, potentially fostering a spectrum of diverse plausible completions. Additionally, the exploration of scalability under constrained computational environments presents viable prospects for refinement.

In conclusion, VRCNet embodies an overview of probabilistic rigor and relational acuity, paving the way for transformative developments in point cloud analytics. Through its dual-path design and innovative relational modules, it delineates a path toward comprehensive and nuanced shape reconstruction, promoting a deeper understanding of 3D data processing intricacies within the broader AI sphere.