3D Object Detection for Autonomous Driving: A Survey (2106.10823v3)

Abstract: Autonomous driving is regarded as one of the most promising remedies to shield human beings from severe crashes. To this end, 3D object detection serves as the core basis of perception stack especially for the sake of path planning, motion prediction, and collision avoidance etc. Taking a quick glance at the progress we have made, we attribute challenges to visual appearance recovery in the absence of depth information from images, representation learning from partially occluded unstructured point clouds, and semantic alignments over heterogeneous features from cross modalities. Despite existing efforts, 3D object detection for autonomous driving is still in its infancy. Recently, a large body of literature have been investigated to address this 3D vision task. Nevertheless, few investigations have looked into collecting and structuring this growing knowledge. We therefore aim to fill this gap in a comprehensive survey, encompassing all the main concerns including sensors, datasets, performance metrics and the recent state-of-the-art detection methods, together with their pros and cons. Furthermore, we provide quantitative comparisons with the state of the art. A case study on fifteen selected representative methods is presented, involved with runtime analysis, error analysis, and robustness analysis. Finally, we provide concluding remarks after an in-depth analysis of the surveyed works and identify promising directions for future work.

• The paper provides a template outlining the standard structure for 3D object detection research in autonomous driving.
• It highlights placeholders for essential elements such as title, abstract, figures, and tables to guide scholarly submissions.
• The document emphasizes clear organization to facilitate empirical analysis and enhance academic communication.

Analysis of Undefined Document Structure

The given document lacks substantive content such as a defined title, author names, an abstract, or any numerical data that would typically contribute to a comprehensive analysis or understanding of a research paper. The absence of detailed sections and substantive academic findings makes it challenging to elucidate specific insights or implications derived from this text.

Despite the document's structural framework (including placeholders for title, author affiliation, abstract, highlights, and references), the content remains unpopulated. Therefore, this text functions as a template, likely intended for future researchers to input their findings, rather than a finished scholarly article ready for analysis.

Key Structural Elements:

• Title and Author Information: The sections designated for the title, authors, and their affiliations are standard, ensuring clear recognition and credibility of the work once complete information is provided.
• Abstract and Highlights: These sections, once completed, will succinctly convey the research's primary objectives and outcomes, aiding readers in quickly assessing the paper's relevance to their interests.
• Figures and Tables: The empty placeholders suggest a planned inclusion of visual data presentations crucial for reinforcing and elucidating research findings.

Implications for Academic Research

Considering the nature of this document as a template, its primary implication lies in its utility as a foundation for structuring research papers. For prospective authors, this template provides a guideline to systematically organize their work to meet academic publication standards.

Conclusion and Future Directions

In conclusion, while the document at hand is not a repository of research findings, it serves as a meta-structure for forthcoming scholarly contributions. For researchers, the future development of this template involves populating it with empirical data, analysis, and insights that advance the field in question. Conclusively, such templates are beneficial to streamline the publication process, contributing to the efficiency and quality of academic communications.