Super Sparse 3D Object Detection (2301.02562v1)

Abstract: As the perception range of LiDAR expands, LiDAR-based 3D object detection contributes ever-increasingly to the long-range perception in autonomous driving. Mainstream 3D object detectors often build dense feature maps, where the cost is quadratic to the perception range, making them hardly scale up to the long-range settings. To enable efficient long-range detection, we first propose a fully sparse object detector termed FSD. FSD is built upon the general sparse voxel encoder and a novel sparse instance recognition (SIR) module. SIR groups the points into instances and applies highly-efficient instance-wise feature extraction. The instance-wise grouping sidesteps the issue of the center feature missing, which hinders the design of the fully sparse architecture. To further enjoy the benefit of fully sparse characteristic, we leverage temporal information to remove data redundancy and propose a super sparse detector named FSD++. FSD++ first generates residual points, which indicate the point changes between consecutive frames. The residual points, along with a few previous foreground points, form the super sparse input data, greatly reducing data redundancy and computational overhead. We comprehensively analyze our method on the large-scale Waymo Open Dataset, and state-of-the-art performance is reported. To showcase the superiority of our method in long-range detection, we also conduct experiments on Argoverse 2 Dataset, where the perception range ($200m$) is much larger than Waymo Open Dataset ($75m$). Code is open-sourced at https://github.com/tusen-ai/SST.

• The paper explains how the IEEEtran.cls demo file simplifies manuscript formatting for IEEE Computer Society journals.
• It provides step-by-step guidance on setting up titles, author lists, abstracts, and bibliographies to meet IEEE submission standards.
• The review discusses future enhancements like multimedia integration and AI-driven style checks to evolve the template for modern research.

A Review of "Bare Advanced Demo of IEEEtran.cls for IEEE Computer Society Journals"

The paper "Bare Advanced Demo of IEEEtran.cls for IEEE Computer Society Journals" authored by Michael Shell, John Doe, and Jane Doe, offers an exposition of the utilization and features of the IEEEtran.cls LaTeX class. It is structured as a comprehensive guide designed to help authors prepare their manuscripts for submission to IEEE Computer Society journals using the specific formatting requirements provided by the IEEEtran.cls template.

Overview and Purpose

The primary objective of this document is to provide a foundational template for authors, facilitating the process of typesetting and ensuring compliance with IEEE's publication standards. The authors assume the role of providing an operational framework that simulates the intricacies associated with manuscript formatting, thereby enabling authors to focus on their content rather than technical details related to layout preparation.

Methodology

The paper serves as a practical demo file rather than a traditional research paper. It walks potential users through basic to advanced functionalities of the IEEEtran.cls template, offering step-by-step instructions and highlighting essential commands and configurations. It includes segments dedicated to title setup, author names, abstract formatting, sectioning, appendices, and bibliographies. This demo file incorporates examples that demonstrate the structural components required for an IEEE paper, effectively guiding users in managing document features like affiliations, peer-review markings, and acknowledgments.

Implications and Potential Impact

The implications for research and academic publication are largely procedural. By providing a standardized formatting tool, the IEEEtran.cls class simplifies the article submission process, reduces formatting inconsistencies, and enhances the overall quality of presentation. This affords researchers more time to concentrate on the substantive aspects of their work, facilitating higher productivity in academic contributions.

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Future Directions

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