DensePoint: Learning Densely Contextual Representation for Efficient Point Cloud Processing (1909.03669v1)

Abstract: Point cloud processing is very challenging, as the diverse shapes formed by irregular points are often indistinguishable. A thorough grasp of the elusive shape requires sufficiently contextual semantic information, yet few works devote to this. Here we propose DensePoint, a general architecture to learn densely contextual representation for point cloud processing. Technically, it extends regular grid CNN to irregular point configuration by generalizing a convolution operator, which holds the permutation invariance of points, and achieves efficient inductive learning of local patterns. Architecturally, it finds inspiration from dense connection mode, to repeatedly aggregate multi-level and multi-scale semantics in a deep hierarchy. As a result, densely contextual information along with rich semantics, can be acquired by DensePoint in an organic manner, making it highly effective. Extensive experiments on challenging benchmarks across four tasks, as well as thorough model analysis, verify DensePoint achieves the state of the arts.

• The paper introduces DensePoint, which leverages densely contextual representation to enhance point cloud processing efficiency and scalability.
• It presents a novel neural architecture that extracts both local and global features to improve performance on benchmark datasets.
• Experimental results demonstrate significant gains in speed and accuracy, making DensePoint a promising approach for robust 3D data analysis.

Review of "Author Guidelines for ICCV Proceedings"

The document at hand established the author guidelines for International Conference on Computer Vision (ICCV) proceedings, a critical resource for authors intending to submit to this conference. While not an empirical paper or research-focused paper, its significance lies in its articulation of the structure and submission protocols that are necessary for ensuring quality and consistency in conference submissions.

Key Components and Criteria

The document provides a comprehensive overview of the essential formatting and submission rules, which can be outlined in several key areas:

1. Language and Length: All submissions must be in English and conform to a defined page limit, specifically eight pages, excluding references. This ensures uniformity and aids the review process by imposing clear boundaries.
2. Anonymity for Blind Review: A blind review process is in place, necessitating that submissions do not reveal author identities. This entails avoiding self-referential language such as "my" or "our" when discussing related work.
3. Formatting Guidelines: Detailed instructions are given for text, margins, and fonts to maintain consistency. Specifics include a two-column format, section headings, and figure caption styles. The use of a 'ruler' in submissions allows reviewers to provide precise feedback by referencing specific lines.
4. Mathematical Notation and References: Authors are encouraged to number equations for ease of reference. References should be both complete and properly formatted, adhering to a clearly defined citation style.
5. Figures and Tables: All visuals must be well-integrated into the text, with clear captions, and should be legible when printed. This ensures that material can be effectively communicated to reviewers and readers.

Implications and Impact

The guidelines play a crucial role in maintaining the rigor and quality of submissions to ICCV, a leading venue for computer vision research. By enforcing stringent formatting and anonymity protocols, the conference ensures that submissions are evaluated based on content merit rather than presentation discrepancies or perceived author biases.

Future Research and Developments

As the field of computer vision continues to evolve, these guidelines may require updates to accommodate new types of research outputs, such as interactive figures or expansive datasets. Additionally, advances in document preparation technologies could influence future iterations of these guidelines, particularly in automating formatting compliance or enhancing the anonymity process.

Overall, while these author guidelines may seem procedural, they serve as an essential framework ensuring the scientific integrity and quality of ICCV conference proceedings. An adherence to these guidelines facilitates a robust peer-review process, which is vital for the advancement of computer vision as a discipline.