Depth information is important for autonomous systems to perceive environments and
estimate their own state. Traditional depth estimation methods, like structure from motion …

In current decades, significant advancements in robotics engineering and autonomous
vehicles have improved the requirement for precise depth measurements. Depth estimation …

Abstract Training a Neural Radiance Field (NeRF) without pre-computed camera poses is
challenging. Recent advances in this direction demonstrate the possibility of jointly …

Monocular depth estimation is fundamental for 3D scene understanding and downstream
applications. However, even under the supervised setup, it is still challenging and ill-posed …

Self-supervised monocular depth estimation that does not require ground truth for training
has attracted attention in recent years. It is of high interest to design lightweight but effective …

It is a grand challenge for an imaging system to simultaneously obtain multi-dimensional
light field information, such as depth and polarization, of a scene for the accurate perception …

We address the problem of estimating a high quality dense depth map from a single RGB
input image. We start out with a baseline encoder-decoder convolutional neural network …

Self-supervised monocular depth estimation is an attractive solution that does not require
hard-to-source depth la-bels for training. Convolutional neural networks (CNNs) have …

We propose a simple yet effective instance segmentation framework, termed CondInst
(conditional convolutions for instance segmentation). Top-performing instance segmentation …

In computer vision, object detection is one of most important tasks, which underpins a few
instance-level recognition tasks and many downstream applications. Recently one-stage …