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RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

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Related Experiment Video

Updated: May 5, 2026

Photorealistic Learned Landscapes for Augmented Reality

Photorealistic Learned Landscapes for Augmented Reality

Published on: June 27, 2025

Dynamic-Parameterized Reconstruction Model for Resource-Aware Spatial Intelligence.

Hongyi Huang¹, Yanni Zhang², Liang Song¹

¹College of Intelligent Robotics and Advanced Manufacturing, Fudan University, Shanghai 200433, China.

Sensors (Basel, Switzerland)

|May 4, 2026

Summary

This summary is machine-generated.

We introduce DyPRSI, a dynamic framework for monocular vehicle 3D reconstruction. It offers adaptable accuracy-latency trade-offs within a single model, enhancing spatial intelligence for autonomous driving.

Keywords:

early exit monocular 3D reconstruction resource-aware inference spatial intelligence

Related Experiment Videos

Last Updated: May 5, 2026

Photorealistic Learned Landscapes for Augmented Reality

Photorealistic Learned Landscapes for Augmented Reality

Published on: June 27, 2025

Area of Science:

Computer Vision
Robotics
3D Reconstruction

Background:

Autonomous driving demands precise 3D geometry from sensors.
Current monocular mesh reconstruction methods lack flexibility for varying computational resources.

Purpose of the Study:

To develop a dynamic-parameterized framework (DyPRSI) for monocular vehicle 3D reconstruction.
To enable multiple accuracy-latency operating points within a single model for resource-aware applications.

Main Methods:

DyPRSI utilizes a shared Res2Net-BiFPN trunk with two early exits (EE1, EE2).
Each exit features a specific mesh parameterization, creating a coarse-to-fine reconstruction hierarchy.
Lightweight keypoint decoding is employed for early exits, while the main branch retains high-accuracy decoding.

Main Results:

DyPRSI-Main achieves competitive reconstruction accuracy.
Early exits (EE1, EE2) significantly reduce inference latency, offering efficient alternatives.
Ablation studies confirm speedup from lightweight heads and stable reconstruction from exit-specific meshes.

Conclusions:

DyPRSI provides a practical solution for monocular vehicle 3D reconstruction.
The framework efficiently balances accuracy and latency for diverse resource constraints in spatial intelligence.