Recent years have witnessed enormous progress in AI-related fields such as computer
vision, machine learning, and autonomous vehicles. As with any rapidly growing field, it …

Stereo matching is one of the longest-standing problems in computer vision with close to 40
years of studies and research. Throughout the years the paradigm has shifted from local …

Recent progress in 3D object detection from single images leverages monocular depth
estimation as a way to produce 3D pointclouds, turning cameras into pseudo-lidar sensors …

Although cameras are ubiquitous, robotic platforms typically rely on active sensors like
LiDAR for direct 3D perception. In this work, we propose a novel self-supervised monocular …

We propose a monocular depth estimation method SC-Depth, which requires only
unlabelled videos for training and enables the scale-consistent prediction at inference time …

In this paper we propose an approach for monocular 3D object detection from a single RGB
image, which leverages a novel disentangling transformation for 2D and 3D detection losses …

Per-pixel ground-truth depth data is challenging to acquire at scale. To overcome this
limitation, self-supervised learning has emerged as a promising alternative for training …

This work presents Kornia--an open source computer vision library which consists of a set of
differentiable routines and modules to solve generic computer vision problems. At its core …

Self-supervised learning shows great potential in monocular depth estimation, using image
sequences as the only source of supervision. Although people try to use the high-resolution …

Self-supervised paradigms for monocular depth estimation are very appealing since they do
not require ground truth annotations at all. Despite the astonishing results yielded by such …