The Power of Light: Improving Synthetic-to-Real Domain Adaptation through Physically-Based Indirect Illumination
arXiv:2606.22574v2 Announce Type: replace-cross Abstract: While synthetic data generation resolves the manual labeling bottleneck in computer vision, minimizing the syn-to-real domain gap requires optimizing rendering variables. This paper presents a systematic study analyzing the impact of lighting configurations and background complexity on object detection performance. We introduce SmartSDG, an automated, reproducible pipeline built on NVIDIA Isaac Sim using Physically-Based Shading (PBS), alongside ILLUM\_INTRUCK, a new multi-object industrial benchmark dataset. Through 18 controlled exper
The increasing complexity of AI models and the critical need for robust, real-world performance are accelerating research into effective synthetic data generation techniques.
This research directly addresses the 'syn-to-real' domain gap, a major bottleneck in deploying computer vision models, significantly improving the efficacy and reducing the cost of AI development.
The ability to generate higher-fidelity synthetic datasets through physically-based rendering will reduce reliance on costly and time-consuming manual real-world data collection and labeling.', 'The improved realism of synthetic data could accelerate the development and deployment of autonomous systems.
- · AI/Computer Vision developers
- · Robotics companies
- · NVIDIA
- · Industrial automation sector
- · Companies reliant on outdated data augmentation techniques
- · Manual data labeling services
More efficient and reliable deployment of AI models in real-world scenarios, particularly for object detection.
Increased adoption of simulation platforms for training autonomous systems, leading to faster innovation cycles.
Enhanced AI capabilities across various industries, from manufacturing to logistics, driving productivity gains and new applications.
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Read at arXiv cs.AI