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

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 …

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

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 …

Traditionally, 3D indoor scene reconstruction from posed images happens in two phases:
per-image depth estimation, followed by depth merging and surface reconstruction …

Self-supervised learning has shown very promising results for monocular depth estimation.
Scene structure and local details both are significant clues for high-quality depth estimation …

While convolutional neural networks have shown a tremendous impact on various computer
vision tasks, they generally demonstrate limitations in explicitly modeling long-range …

Abstract 3D occupancy prediction is an important task for the robustness of vision-centric
autonomous driving which aims to predict whether each point is occupied in the surrounding …

In this paper, we propose Recurrent Multi-Scale Feature Modulation (R-MSFM), a new deep
network architecture for self-supervised monocular depth estimation. R-MSFM extracts per …