PointDSC: Robust Point Cloud Registration using Deep Spatial Consistency (2103.05465v1)

Abstract: Removing outlier correspondences is one of the critical steps for successful feature-based point cloud registration. Despite the increasing popularity of introducing deep learning methods in this field, spatial consistency, which is essentially established by a Euclidean transformation between point clouds, has received almost no individual attention in existing learning frameworks. In this paper, we present PointDSC, a novel deep neural network that explicitly incorporates spatial consistency for pruning outlier correspondences. First, we propose a nonlocal feature aggregation module, weighted by both feature and spatial coherence, for feature embedding of the input correspondences. Second, we formulate a differentiable spectral matching module, supervised by pairwise spatial compatibility, to estimate the inlier confidence of each correspondence from the embedded features. With modest computation cost, our method outperforms the state-of-the-art hand-crafted and learning-based outlier rejection approaches on several real-world datasets by a significant margin. We also show its wide applicability by combining PointDSC with different 3D local descriptors.

• The paper presents a novel deep spatial consistency module that robustly registers point clouds.
• It employs a deep neural network to capture geometric relationships, reducing registration errors significantly.
• Experimental results demonstrate enhanced accuracy over traditional methods, highlighting its potential for practical applications.

Review of "LaTeX Author Guidelines for CVPR Proceedings"

The paper "LaTeX Author Guidelines for CVPR Proceedings" ostensibly offers a comprehensive guide for authors preparing manuscripts for submission to the Computer Vision and Pattern Recognition (CVPR) conference. While it is inherently formulaic, this paper plays a crucial role in ensuring uniformity and adherence to specified standards in document preparation, a significant aspect of academic publishing.

Manuscript Composition

The document presents detailed guidance concerning the composition of manuscripts in \LaTeX\ for CVPR. It is imperative for manuscripts to maintain uniformity in language, format, and style, aspects which this guide addresses meticulously. The paper starts by emphasizing the requirement for manuscripts to be written in English and proceeds to elaborate on various submission protocols, particularly regarding the dual submission policy, which requires authors to manage manuscripts submitted to multiple conferences attentively.

Document Structure and Formatting

The paper outlines the submission requirements confined to eight pages, excluding references but mandating strict compliance to margins, fonts, and layout specifications. Distinct from some other conferences, CVPR does not impose extra page charges, a fact highlighted within the document. Overlength submissions do not qualify for review, underscoring the necessity for authors to rigorously adhere to page limits.

A notable aspect of the guidelines is the inclusion of a unique ruler in the review version to aid reviewers by marking precise lines for citation. However, this should be removed for the final camera-ready copy. This aspect highlights CVPR's specificity in creating an efficient review process.

Review Process and Anonymity

A critical section addresses the concept of blind review, providing guidance on maintaining author anonymity. Authors must avoid self-identification through phrases like "our previous work," advising instead to cite their work impersonally. This guidance reflects a nuanced understanding of maintaining author anonymity while allowing necessary self-citations, thus balancing the dual objectives of anonymity and acknowledgment of prior work.

Technical Details and Examples

The paper also explores technical typesetting details, such as managing mathematical notations, fonts, and figure placements, fostering consistency. The suggestion to use macros for consistent citation formats reveals the emphasis on technical precision. The guidelines assimilate minor, yet crucial, formatting aspects such as equation numbering and the proper use of footnotes, which often evade attention but are essential for professional presentation.

Discussion

From a broader perspective, this paper serves as a pivotal tool for ensuring clarity, precision, and uniformity in academic document preparation. While it doesn't present novel technical advancements, its importance lies in its role as a vital reference for academic practitioners seeking to publish within CVPR's esteemed purview. As the document sets a standard for what is arguably a foundational component in the dissemination of research, it indirectly supports the genesis of clarity and readability in the broader research community's outputs.

Conclusion

In conclusion, while the content of "LaTeX Author Guidelines for CVPR Proceedings" may appear mundane compared to research-focused papers, the guidelines serve as an indispensable instrument for authors in computer vision and related fields. The adherence to these guidelines can have implications for document acceptance and subsequent academic discourse.

Future Directions

A move toward more sophisticated document formatting tools integrated within popular script development environments could streamline adherence to such guidelines. Automation in detecting issues related to dual submissions or over-length manuscripts might further improve the submission process, as well as adoption of tools for better managing blind review processes without compromising on discoverability and citation networks.

Thus, while not advancing technical research boundaries, the paper is monumental in its contribution to maintaining scholarly communication's integrity and consistency.