Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Uniform Depth Channel Flow: Problem Solving

Uniform Depth Channel Flow: Problem Solving

To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Determination of non-volatile metabolic profiles and their sensory relevance in different grades of brandy through widely targeted metabolomics.

Food chemistry: X·2026

Same author

Atlas of predicted protein complex structures across kingdoms.

Nature communications·2026

Same author

The Clinical Utility of Whole-Exome Sequencing in the Prenatal Diagnosis of Fetal Skeletal Dysplasia.

International journal of women's health·2026

Same author

Accurate Industrial Anomaly Detection and Localization Using Weakly-Supervised Residual Transformers.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Same author

Study of Ultrasound Diagnosis of Acrania-Exencephaly-Anencephaly Sequence in Middle First Trimester: A Multicenter Center, Retrospective Analysis.

Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine·2025

Same author

Diffusion Models are Efficient Data Generators for Human Mesh Recovery.

IEEE transactions on pattern analysis and machine intelligence·2025

Same journal

HardFlow: Hard-Constrained Sampling for Flow-Matching Models Via Trajectory Optimization.

IEEE transactions on pattern analysis and machine intelligence·2026

Same journal

Industrial Brain: Self-Evolving Neuro-Symbolic Autonomy with Causal Resilience for Cyber-Physical Systems.

IEEE transactions on pattern analysis and machine intelligence·2026

Same journal

Adaptive Hardness-Driven Dictionary Distillation for Incomplete Streaming View Clustering.

IEEE transactions on pattern analysis and machine intelligence·2026

Same journal

Mixture of Global and Local Experts with Diffusion Transformer for Controllable Face Generation.

IEEE transactions on pattern analysis and machine intelligence·2026

Same journal

Task-KV: Task-aware KV Cache Optimization via Semantic Differentiation of Attention Heads.

IEEE transactions on pattern analysis and machine intelligence·2026

Same journal

Achieving Text-based Person Retrieval with Any Granularity.

IEEE transactions on pattern analysis and machine intelligence·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Oct 9, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Auto-Rectify Network for Unsupervised Indoor Depth Estimation.

Jia-Wang Bian, Huangying Zhan, Naiyan Wang

IEEE Transactions on Pattern Analysis and Machine Intelligence

|December 17, 2021

Summary

This summary is machine-generated.

This study addresses challenges in single-view depth estimation from unlabelled videos, particularly for handheld devices. We introduce methods to handle complex camera motions, improving depth estimation accuracy in diverse indoor and outdoor scenes.

More Related Videos

Photorealistic Learned Landscapes for Augmented Reality

Photorealistic Learned Landscapes for Augmented Reality

Published on: June 27, 2025

Related Experiment Videos

Last Updated: Oct 9, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Photorealistic Learned Landscapes for Augmented Reality

Photorealistic Learned Landscapes for Augmented Reality

Published on: June 27, 2025

Area of Science:

Computer Vision
Machine Learning

Background:

Single-view depth estimation from unlabelled videos shows promise, but struggles with complex ego-motions common in handheld device footage.
Existing methods excel in driving scenes but fail in indoor environments due to rotational noise during training.

Purpose of the Study:

To identify and address the impact of complex ego-motions on unsupervised depth estimation from unlabelled videos.
To develop robust methods for accurate depth estimation in challenging handheld video settings.

Main Methods:

Proposed a data pre-processing technique to rectify images by removing relative rotations, enhancing supervised signals.
Developed an Auto-Rectify Network with novel loss functions for end-to-end learning and automatic image rectification during training.

Main Results:

Significantly improved depth estimation performance on the NYUv2 dataset compared to prior unsupervised state-of-the-art (SOTA) methods.
Demonstrated generalization across diverse datasets including ScanNet, Make3D, 7-Scenes, and KITTI.

Conclusions:

Complex ego-motions, especially rotations, are critical obstacles in unsupervised depth learning from handheld videos.
The proposed rectification methods effectively improve depth estimation accuracy and generalize well to various environments.