Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of
uncertainties during both optimization and decision making processes. They have been …

Depth estimation is a classic task in computer vision, which is of great significance for many
applications such as augmented reality, target tracking and autonomous driving. Traditional …

We introduce DROID-SLAM, a new deep learning based SLAM system. DROID-SLAM
consists of recurrent iterative updates of camera pose and pixelwise depth through a Dense …

In this work, we present a new multi-view depth estimation method that utilizes both
conventional SfM reconstruction and learning-based priors over the recently proposed …

Monocular depth estimation (MDE) is a fundamental task in computer vision and has drawn
increasing attention. Recently, some methods reformulate it as a classification-regression …

We present a method that learns a spatiotemporal neural irradiance field for dynamic scenes
from a single video. Our learned representation enables free-viewpoint rendering of the …

We present a novel framework named NeuralRecon for real-time 3D scene reconstruction
from a monocular video. Unlike previous methods that estimate single-view depth maps …

Monocular 3D object detection has received increasing attention due to the wide application
in autonomous driving. Existing works mainly focus on introducing geometry projection to …

Self-supervised monocular depth estimation networks are trained to predict scene depth
using nearby frames as a supervision signal during training. However, for many …

Monocular depth estimation is vital for scene understanding and downstream tasks. We
focus on the supervised setup, in which ground-truth depth is available only at training time …