Neural-Sim: Learning to Generate Training Data with NeRF
"Traditional approaches for training a computer vision models requires collecting and labelling vast amounts of imagery under a diverse set of scene configurations and properties. This process is incredibly time-consuming, and it is challenging to ensure that the captured data distribution maps well to the target domain of an application scenario. In recent years, synthetic data has emerged as a way to address both of these issues. However, current approaches either require human experts to manually tune each scene property or use automatic methods that provide little to no control; this requires rendering large amounts random data variations, which is slow and is often suboptimal for the target domain. We present the first fully differentiable synthetic data generation pipeline that uses Neural Radiance Fields (NeRFs) in a closed-loop with a target application’s loss function to generate data, on demand, with no human labor, to maximise accuracy for a target task. We illustrate the effectiveness of our method with synthetic and real-world object detection experiments. In addition, we evaluate on a new ""YCB-in-the-Wild"" dataset that provides a test scenario for object detection with varied pose in real-world environments."