DepthNet: A Monocular Depth Estimation Framework

Anunay,; Pankaj,; Dhiman, Chhavi

doi:10.1109/iceet53442.2021.9659770

Cited by 2 publications

(1 citation statement)

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“…LiteMono (Zhang et al, 2022) is a lightweight architecture designed to extract effective features from input images using a lightweight encoder. The proposed architecture includes an encoder-decoder DepthNet (Anunay et al, 2021) and a PoseNet (Kendall et al, 2015) that estimate multi-scale inverse depth maps and camera motion between adjacent frames, respectively. The architecture also uses consecutive dilated convolutions (CDC) to enhance local features and a Local-Global Features Interaction (LGFI) module to model long-range information efficiently.…”

Section: Monocular Depth Estimationmentioning

confidence: 99%

Mono-Hydra: Real-Time 3d Scene Graph Construction From Monocular Camera Input With Imu

Udugama,

Vosselman,

Nex

2023

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. The ability of robots to autonomously navigate through 3D environments depends on their comprehension of spatial concepts, ranging from low-level geometry to high-level semantics, such as objects, places, and buildings. To enable such comprehension, 3D scene graphs have emerged as a robust tool for representing the environment as a layered graph of concepts and their relationships. However, building these representations using monocular vision systems in real-time remains a difficult task that has not been explored in depth.This paper puts forth a real-time spatial perception system Mono-Hydra, combining a monocular camera and an IMU sensor setup, focusing on indoor scenarios. However, the proposed approach is adaptable to outdoor applications, offering flexibility in its potential uses. The system employs a suite of deep learning algorithms to derive depth and semantics. It uses a robocentric visual-inertial odometry (VIO) algorithm based on square-root information, thereby ensuring consistent visual odometry with an IMU and a monocular camera. This system achieves sub-20 cm error in real-time processing at 15 fps, enabling real-time 3D scene graph construction using a laptop GPU (NVIDIA 3080). This enhances decision-making efficiency and effectiveness in simple camera setups, augmenting robotic system agility. We make Mono-Hydra publicly available at: https://github.com/UAV-Centre-ITC/Mono_Hydra.

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Section: Monocular Depth Estimationmentioning

confidence: 99%