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Depth Perception and Spatial Vision01:15

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Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
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Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring
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Beyond Sparsity: Receptive Field Expansion and Cross-Task Fusion for LiDAR Multi-Task Perception.

Shengjie Huang, Runbang Zhang, Shuo Liu

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |April 29, 2026
    PubMed
    Summary

    This study introduces a new framework for LiDAR-based multi-task perception in autonomous driving, improving 3D object detection and semantic segmentation by expanding receptive fields and fusing cross-task information.

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    Topographical Estimation of Visual Population Receptive Fields by fMRI
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    Area of Science:

    • Computer Vision
    • Robotics
    • Artificial Intelligence

    Background:

    • LiDAR-based multi-task perception is crucial for autonomous driving.
    • Existing methods face challenges with restricted receptive fields and suboptimal task interaction.

    Purpose of the Study:

    • To present a novel multi-task framework for enhanced 3D object detection and semantic segmentation.
    • To overcome limitations of restricted receptive fields and improve cross-task information integration.

    Main Methods:

    • Introduced the Spatial Density-Invariant Multi-scale Integrator (SDIMI) for adaptive multi-resolution feature fusion.
    • Developed the Synergistic Instance-Driven Multitask Fusion (SIDMF) module for dynamic instance-level feature alignment.
    • Leveraged receptive field expansion and cross-task fusion beyond sparsity constraints.

    Main Results:

    • Achieved 72.0% NuScenes Detection Score (NDS) and 84.4% mIOU on the NuScenes dataset.
    • Obtained 79.8% mAPH-L2 and 72.3% mIOU on the Waymo Open Dataset.
    • Demonstrated state-of-the-art performance on benchmark datasets.

    Conclusions:

    • The proposed framework effectively enhances LiDAR-based multi-task perception.
    • The SDIMI and SIDMF modules significantly improve feature integration and propagation for detection and segmentation.