Distillation Style Regulators and Semantic Prior-Guided Framework for Non-Ideal Single-View 3D Vehicle Point Cloud...

Area of Science:

• Computer Vision
• 3D Reconstruction
• Machine Learning

Background:

• Autonomous driving systems require extensive 3D vehicle datasets for testing.
• Current methods for 3D asset generation are time-consuming and costly.
• Efficient construction of 3D vehicle models is a critical bottleneck.

Purpose of the Study:

• To develop a cost-effective and convenient framework for 3D vehicle asset generation.
• To enable accurate and robust vehicle point cloud reconstruction from single-view photographs.
• To overcome limitations of current labor-intensive modeling and multi-view capture pipelines.

Main Methods:

• A semantic prior-guided framework utilizing a diffusion backbone.
• Incorporation of geometric and appearance priors from camera-aware image features, masks, and distance-transform maps.
• Implementation of distillation-style regulators (pretrained neural networks) for semantic knowledge transfer and regularization.

Main Results:

• The framework successfully reconstructs high-quality 3D point cloud assets from single-view images.
• Achieved superior performance compared to state-of-the-art point cloud baselines on the 3DRealCar++ dataset.
• Demonstrated significant improvements in F-score and Chamfer Distance metrics.

Conclusions:

• The proposed framework effectively addresses the bottleneck in 3D vehicle asset generation.
• Leveraging semantic priors enables accurate reconstruction from limited visual information.
• This method facilitates the creation of diverse and realistic 3D assets for autonomous driving simulation.