NFL: Normal Field Learning for 6-DoF Grasping of Transparent Objects

We present Normal Field Learning (NFL), a robust yet practical solution to perceive 3D layouts of transparent objects and grasp them quickly. Conventional input modalities for vision-based grasping do not provide sufficient information for transparent objects. However, with the recent advance on datasets and algorithms for transparent objects, we can at least obtain noisy estimates of normals and masks for various real-world conditions. Instead of directly using the RGB images, we propose to use the estimates to train a neural volume, which serves as an intermediate representation ignorant of challenging appearance variations. We formulate the training objective to account for inherent uncertainty in individual estimation, and together with the volumetric aggregation, we can reliably extract useful geometric information for grasping. Our neural volume deploys a voxel-grid based representation, motivated by acceleration techniques of neural radiance fields. However, we directly store the normal and density values in the grid cells instead of latent features. Our modification allows direct access to the geometric values without additional inference or volume rendering, further enhancing the efficiency. Our results show over 85% success rates in grasping in cluttered scenes with only 40 seconds of training time.