Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Robust 3D Semantic Occupancy Prediction With Calibration-Free Spatial Transformation.

Zhuangwei Zhuang, Ziyin Wang, Sitao Chen

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |July 7, 2026
    PubMed
    Summary

    This study introduces a Robust and Efficient 3D semantic Occupancy (REO) prediction scheme for autonomous driving. REO offers faster, calibration-free 3D scene understanding by using attention mechanisms for improved spatial correspondence.

    Related Concept Videos

    Calibration Curves: Linear Least Squares01:20

    Calibration Curves: Linear Least Squares

    A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
    For data that follow a straight line, the standard method for fitting is the linear...

    You might also read

    Related Articles

    Articles linked to this work by shared authors, journal, and citation graph.

    Sort by
    Same author

    Correlation of Lgr5 expression with clinicopathological features of colorectal cancer and its diagnostic and prognostic values.

    Journal of B.U.ON. : official journal of the Balkan Union of Oncology·2021
    Same author

    Optically pumped NMR oscillator based on <sup>131</sup>Xe nuclear spins.

    Journal of magnetic resonance (San Diego, Calif. : 1997)·2021
    Same author

    Generation of patient-specific induced pluripotent stem cell line (CSUi002-A) from a patient with isolated dystonia carrying TOR1A mutation.

    Stem cell research·2021
    Same author

    Synthesis of Fluorescent Carbon Dots and Their Application in Ascorbic Acid Detection.

    Molecules (Basel, Switzerland)·2021
    Same author

    Clinical features and outcomes of anti-neutrophil cytoplasmic autoantibody-associated vasculitis in Chinese elderly and very elderly patients.

    International urology and nephrology·2021
    Same author

    Identification of Potential Driver Genes Based on Multi-Genomic Data in Cervical Cancer.

    Frontiers in genetics·2021

    Area of Science:

    • Computer Vision
    • Robotics
    • Artificial Intelligence

    Background:

    • 3D semantic occupancy prediction is crucial for autonomous driving systems, requiring multi-sensor data fusion.
    • Existing 2D-to-3D methods rely on precise sensor calibration, limiting real-world application and computational efficiency.

    Purpose of the Study:

    • To develop a Robust and Efficient 3D semantic Occupancy (REO) prediction scheme.
    • To overcome limitations of calibration dependency and computational cost in current 3D occupancy prediction methods.

    Main Methods:

    • Proposed a calibration-free spatial transformation using vanilla attention to model 2D-to-3D correspondence.
    • Introduced a multi-task training framework to enhance feature discrimination and spatial correspondence learning.
    • Developed a query-based prediction scheme for efficient, large-scale, fine-grained occupancy predictions.

    Related Experiment Videos

    Main Results:

    • REO demonstrates superior performance on OpenOccupancy, Occ3D-nuScenes, and SemanticKITTI Scene Completion benchmarks.
    • Achieved a 19.8x speedup compared to Co-Occ with a 1.1% improvement in geometry IoU on OpenOccupancy.
    • Successfully projected 2D features to a Bird's-Eye View (BEV) plane without sensor calibration input.

    Conclusions:

    • The proposed REO scheme offers a robust and efficient solution for 3D semantic occupancy prediction.
    • Calibration-free approach enhances applicability in complex autonomous driving environments.
    • The method significantly improves speed and accuracy, making it suitable for real-time onboard deployment.