The content discusses the challenges of obtaining real-world training data for robotic systems and introduces COV-NeRF as a solution. COV-NeRF generates photorealistic renderings and various types of supervision to close the sim-to-real gap effectively. The method is compared to existing NeRF models and evaluated in challenging scenarios like bin-picking applications.
The article highlights the importance of perception in robotics, emphasizing deep learning methods' reliance on training data. It introduces COV-NeRF as a novel approach to synthesizing training data targeted at real-world scenes and objects. By extracting objects from real images and composing them into new scenes, COV-NeRF aims to improve model performance across various perceptual modalities.
COV-NeRF is presented as a solution to the sim-to-real gap issue faced by deep learning models in robotics. The method's ability to generate targeted synthetic data based on real-world scenes is highlighted, showcasing improvements in depth estimation, object detection, instance segmentation, and shape completion tasks. The application of COV-NeRF in a bin-picking scenario demonstrates its effectiveness in reducing the sim-to-real gap and enhancing overall system performance.
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by Nikhil Mishr... at arxiv.org 03-08-2024
https://arxiv.org/pdf/2403.04114.pdfDeeper Inquiries