3D Data Augmentation

3D data augmentation enhances the training of deep learning models for tasks involving 3D data, such as object detection in autonomous driving and scene understanding, by artificially increasing the diversity and quantity of training examples. Current research focuses on developing effective 3D augmentation techniques tailored to different data modalities (e.g., LiDAR point clouds, RGB-D images) and model architectures (e.g., convolutional neural networks, transformers), often leveraging techniques like Neural Radiance Fields (NeRFs) for realistic scene generation or search-based optimization for efficient augmentation policy discovery. These advancements are crucial for improving the robustness and accuracy of 3D perception systems in various applications, particularly where acquiring sufficient real-world labeled data is challenging or expensive.