DPC: Unsupervised Deep Point Correspondence via Cross and Self Construction (2110.08636v1)

Abstract: We present a new method for real-time non-rigid dense correspondence between point clouds based on structured shape construction. Our method, termed Deep Point Correspondence (DPC), requires a fraction of the training data compared to previous techniques and presents better generalization capabilities. Until now, two main approaches have been suggested for the dense correspondence problem. The first is a spectral-based approach that obtains great results on synthetic datasets but requires mesh connectivity of the shapes and long inference processing time while being unstable in real-world scenarios. The second is a spatial approach that uses an encoder-decoder framework to regress an ordered point cloud for the matching alignment from an irregular input. Unfortunately, the decoder brings considerable disadvantages, as it requires a large amount of training data and struggles to generalize well in cross-dataset evaluations. DPC's novelty lies in its lack of a decoder component. Instead, we use latent similarity and the input coordinates themselves to construct the point cloud and determine correspondence, replacing the coordinate regression done by the decoder. Extensive experiments show that our construction scheme leads to a performance boost in comparison to recent state-of-the-art correspondence methods. Our code is publicly available at https://github.com/dvirginz/DPC.

• The paper introduces an unsupervised framework that leverages per-point latent embeddings for accurate dense correspondence in non-rigid 3D point clouds.
• It employs innovative cross and self construction modules to robustly align point clouds in real-time without relying on mesh connectivity or extensive training data.
• Empirical results demonstrate significant improvements in correspondence accuracy and processing speed across diverse datasets, enhancing practical 3D computer vision applications.

An Analysis of "DPC: Unsupervised Deep Point Correspondence via Cross and Self Construction"

The paper "DPC: Unsupervised Deep Point Correspondence via Cross and Self Construction" by Lang et al. introduces an innovative approach for solving non-rigid dense correspondence between point clouds, a central problem in 3D computer vision. The proposed method, termed Deep Point Correspondence (DPC), provides a significant improvement over traditional spectral and spatial methodologies by eschewing the dependency on large training datasets and connectivity information, making it well-suited for real-time applications.

Theoretical Foundations and Contributions

The core challenge addressed by DPC is the dense alignment of point clouds, a task complicated by the variability in non-rigid shapes. Previous methods predominantly fell into two categories: spectral-based approaches and spatial encoder-decoder frameworks. Spectral techniques, while successful on synthetic datasets, confront limitations related to computational burden and instability in real environments due to their reliance on mesh connectivity and high processing times. On the other hand, spatial approaches necessitated large data for training and demonstrated poor generalization across datasets.

DPC circumvents these issues by introducing a novel framework that relies solely on learned latent feature embeddings and spatial construction without the necessity for a decoder. The paper presents three significant methodological advances:

1. Per-point Feature Embedding: Utilizing a variant of the DGCNN architecture, DPC generates high-dimensional embeddings for each point in the cloud. This embedding is crucial for defining the latent space where correspondences between point clouds are established.
2. Cross and Self Construction: Instead of regressing point coordinates, DPC utilizes a similarity measure between learned latent embeddings to perform cross and self-constructions. This approach employs two innovative modules:
   • Cross-construction Module: It approximates corresponding points between two clouds by determining a weighted average based on local similarity in the latent space.
   • Self-construction Module: This regularizer promotes smoothness by ensuring similar features for neighboring points within a shape, thereby enhancing the robustness of the correspondence.
3. Unsupervised Learning Framework: DPC's training does not rely on direct supervision of match annotations, leveraging its construction modules to refine point features. As a result, it requires significantly less training data while improving generalization across datasets.

Empirical Results and Implications

The experimental results presented are compelling. DPC achieves substantial performance gains in terms of correspondence accuracy and error metrics on well-established datasets involving human figures (SURREAL and SHREC'19) and animals (SMAL and TOSCA). The robustness of the proposed approach is evidenced by its ability to maintain high accuracy even on test sets with resolutions different from the training data.

Remarkably, DPC exhibits real-time processing capabilities, handling 38 pairs of point clouds per second, which aligns with practical deployment scenarios in various applications such as motion tracking and object recognition.

Future Directions

The implications of this research extend into multiple domains within and beyond computer vision. The potential for DPC to handle non-standard datasets without the extensive preprocessing or connectivity information opens avenues for deploying 3D sensing in fields like augmented reality, robotics, and autonomous systems.

Further research could explore the integration of this framework with temporal data, extending its utility to dynamic scenes where the temporal coherence of point clouds could be leveraged for even more robust correspondence. Additionally, investigating the architecture's adaptability to different sensor modalities and enhancing its robustness to noise and partial observation conditions would continue to broaden its application spectrum.

Conclusion

"DPC: Unsupervised Deep Point Correspondence via Cross and Self Construction" represents a noteworthy advancement in unsupervised correspondence methods for 3D point clouds. By eliminating the reliance on large labeled datasets and delivering enhanced generalization and real-time processing, DPC holds promise for expanding the efficacy of 3D computer vision tasks in both academic and applied research settings.