Consistent Depth Prediction for Transparent Object Reconstruction from RGB-D Camera

Transparent objects are commonly seen in indoor scenes but are hard to estimate. Currently, commercial depth cameras face difficulties in estimating the depth of transparent objects due to the light reflection and refraction on their surface. As a result, they tend to make a noisy and incorrect depth value for transparent objects. These incorrect depth data make the traditional RGB-D SLAM method fails in reconstructing the scenes that contain transparent objects. An exact depth value of the transparent object is required to restore in advance and it is essential that the depth value of the transparent object must keep consistent in different views, or the reconstruction result will be distorted. Previous depth prediction methods of transparent objects can restore these missing depth values but none of them can provide a good result in reconstruction due to the inconsistency prediction. In this work, we propose a real-time reconstruction method using a novel stereo-based depth prediction network to keep the consistency of depth prediction in a sequence of images. Because there is no video dataset about transparent objects currently to train our model, we construct a synthetic RGB-D video dataset with different transparent objects. Moreover, to test generalization capability, we capture video from real scenes using the RealSense D435i RGB-D camera. We compare the metrics on our dataset and SLAM reconstruction results in both synthetic scenes and real scenes with the previous methods. Experiments show our significant improvement in accuracy on depth prediction and scene reconstruction.