SlowGuess/NeurIPS_2023_OCR huggingface dataset:
Limitations. Our method, while effective, does have certain limitations. A key constraint is its reliance on the availability of external 3D objects, particularly for uncommon categories where sufficient 3D assets may not be readily available. This limitation could potentially impact the performance of downstream tasks. Moreover, the quality of inserted objects can also affect the results. Possible strategies to address this limitation could include leveraging techniques like Neural Radiance Fields (NeRF) to construct higher-quality 3D assets for different categories.
Broader Impact. Our proposed 3D Copy-Paste method demonstrate that physically plausible 3D object insertion can serve as an effective generative data augmentation technique, leading to state-of-the-art performance in discriminative downstream tasks like monocular 3D object detection. The implications of this work are profound for both the computer graphics and computer vision communities. From a graphics perspective, our method demonstrates that more accurate 3D property estimation, reconstruction, and inverse rendering techniques can generate more plausible 3D assets and better scene understanding. These assets not only look visually compelling but can also effectively contribute to downstream computer vision tasks. From a computer vision perspective, it encourages us to utilize synthetic data more effectively to tackle challenges in downstream fields, including computer vision and robotics.