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 …

Image guided depth completion is the task of generating a dense depth map from a sparse
depth map and a high quality image. In this task, how to fuse the color and depth modalities …

Depth maps produced by LiDAR-based approaches are sparse. Even high-end LiDAR
sensors produce highly sparse depth maps, which are also noisy around the object …

Depth completion deals with the problem of recovering dense depth maps from sparse ones,
where color images are often used to facilitate this task. Recent approaches mainly focus on …

Dense depth perception is critical for autonomous driving and other robotics applications.
However, modern LiDAR sensors only provide sparse depth measurement. It is thus …

Depth completion aims to recover a dense depth map from the sparse depth data and the
corresponding single RGB image. The observed pixels provide the significant guidance for …

We introduce a new diffusion-based approach for shape completion on 3D range scans.
Compared with prior deterministic and probabilistic methods, we strike a balance between …

Depth completion aims to recover a dense depth map from a sparse depth map with the
corresponding color image as input. Recent approaches mainly formulate the depth …

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 …

Depth completion starts from a sparse set of known depth values and estimates the
unknown depths for the remaining image pixels. Most methods model this as depth …