In recent years, Simultaneous Localization and Mapping (SLAM) systems have shown
significant performance, accuracy, and efficiency gain. In this regard, Visual Simultaneous …

Abstract This work presents Depth Anything a highly practical solution for robust monocular
depth estimation. Without pursuing novel technical modules we aim to build a simple yet …

We introduce PointOdyssey, a large-scale synthetic dataset, and data generation framework,
for the training and evaluation of long-term fine-grained tracking algorithms. Our goal is to …

We propose a novel geometric and photometric 3D mapping pipeline for accurate and real-
time scene reconstruction from casually taken monocular images. To achieve this, we …

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 …

We present a unified formulation and model for three motion and 3D perception tasks:
optical flow, rectified stereo matching and unrectified stereo depth estimation from posed …

We introduce dense prediction transformers, an architecture that leverages vision
transformers in place of convolutional networks as a backbone for dense prediction tasks …

Due to the limitations of LiDAR, such as its high cost, short service life and massive volume,
visual sensors with their lightweight and low cost are attracting more and more attention and …

Neural network based computer vision systems are typically built on a backbone, a
pretrained or randomly initialized feature extractor. Several years ago, the default option was …

Monocular depth estimation is scale-ambiguous, and thus requires scale supervision to
produce metric predictions. Even so, the resulting models will be geometry-specific, with …