Depth completion aims at predicting dense pixel-wise depth from an extremely sparse map
captured from a depth sensor, eg, LiDARs. It plays an essential role in various applications …

We present a method for inferring dense depth from a camera image and a sparse noisy
radar point cloud. We first describe the mechanics behind mmWave radar point cloud …

We propose a large-scale dataset of real-world rainy and clean image pairs and a method to
remove degradations, induced by rain streaks and rain accumulation, from the image. As …

Today single image deweathering is arguably more sensitive to the dataset type, rather than
the model. We introduce WeatherStream, an automatic pipeline capturing all real-world …

We present a method for inferring dense depth maps from images and sparse depth
measurements by leveraging synthetic data to learn the association of sparse point clouds …

Recovering a dense depth image from sparse LiDAR scans is a challenging task. Despite
the popularity of color-guided methods for sparse-to-dense depth completion, they treated …

Unsupervised depth completion and estimation methods are trained by minimizing
reconstruction error. Block artifacts from resampling, intensity saturation, and occlusions are …

Abstract Three-dimensional (3D) reconstruction from a single image is an ill-posed problem
with inherent ambiguities ie scale. Predicting a 3D scene from text description (s) is similarly …

We propose a method to infer a dense depth map from a single image, its calibration, and
the associated sparse point cloud. In order to leverage existing models (teachers) that …

It is common to observe performance degradation when transferring models trained on
some (source) datasets to target testing data due to a domain gap between them. Existing …