LAUSR

The objective of depth completion is to generate a dense depth map by upsampling a sparse one. However, irregular sparse patterns or the lack of groundtruth data caused by unstructured data make depth completion extremely challenging. Sensor fusion using both RGB and LIDAR sensors can help produce a more reliable context with higher accuracy. Compared with previous approaches, this method takes semantic segmentation images as additional input and develops an unsupervised loss function. Thus, when combined with supervised depth loss, the depth completion problem is considered as semi-supervised learning. We used an adapted Wasserstein Generative Adversarial Network architecture instead of applying the traditional autoencoder approach and post-processing process to preserve valid depth measurements received from the input and further enhance the depth value precision of the results. Our proposed method was evaluated on the KITTI depth completion benchmark, and its performance in depth completion was proven to be competitive.