• Home
  • About Us
  • Contact Us
  • Disclaimer
  • Privacy Policy
Tuesday, July 8, 2025
newsaiworld
  • Home
  • Artificial Intelligence
  • ChatGPT
  • Data Science
  • Machine Learning
  • Crypto Coins
  • Contact Us
No Result
View All Result
  • Home
  • Artificial Intelligence
  • ChatGPT
  • Data Science
  • Machine Learning
  • Crypto Coins
  • Contact Us
No Result
View All Result
Morning News
No Result
View All Result
Home Artificial Intelligence

Monocular Depth Estimation with Depth Something V2 | by Avishek Biswas | Jul, 2024

Admin by Admin
July 24, 2024
in Artificial Intelligence
0
1721853188 1tvojnqhjjquwwvmimhpfgq.png
0
SHARES
1
VIEWS
Share on FacebookShare on Twitter


How do neural networks be taught to estimate depth from 2D photos?

Avishek Biswas

Towards Data Science

10 min learn

·

14 hours in the past

What’s Monocular Depth Estimation?

The Depth Something V2 Algorithm (Illustration by Writer)

Monocular Depth Estimation (MDE) is the duty of coaching a neural community to find out depth data from a single picture. That is an thrilling and difficult space of Machine Studying and Laptop Imaginative and prescient as a result of predicting a depth map requires the neural community to type a three-d understanding from only a 2-dimensional picture.

On this article, we’ll talk about a brand new mannequin known as Depth Something V2 and its precursor, Depth Something V1. Depth Something V2 has outperformed almost all different fashions in Depth Estimation, exhibiting spectacular outcomes on difficult photos.

Depth Something V2 Demo (Supply: Display recording by the writer from Depth Something V2 DEMO web page)

This text relies on a video I made on the identical subject. Here’s a video hyperlink for learners preferring a visible medium. For individuals who want studying, proceed!

Why ought to we even care about MDE fashions?

Good MDE fashions have many sensible makes use of, akin to aiding navigation and impediment avoidance for robots, drones, and autonomous automobiles. They may also be utilized in video and picture enhancing, background alternative, object elimination, and creating 3D results. Moreover, they’re helpful for AR and VR headsets to create interactive 3D areas across the consumer.

There are two predominant approaches for doing MDE (this text solely covers one)

Two predominant approaches have emerged for coaching MDE fashions — one, discriminative approaches the place the community tries to foretell depth as a supervised studying goal, and two, generative approaches like conditional diffusion the place depth prediction is an iterative picture era process. Depth Something belongs to the primary class of discriminative approaches, and that’s what we might be discussing right now. Welcome to Neural Breakdown, and let’s go deep with Depth Estimation[!

To fully understand Depth Anything, let’s first revisit the MiDAS paper from 2019, which serves as a precursor to the Depth Anything algorithm.

Source: Screenshot taken from the MIDAS Paper (License: Free)

MiDAS trains an MDE model using a combination of different datasets containing labeled depth information. For instance, the KITTI dataset for autonomous driving provides outdoor images, while the NYU-Depth V2 dataset offers indoor scenes. Understanding how these datasets are collected is crucial because newer models like Depth Anything and Depth Anything V2 address several issues inherent in the data collection process.

How real-world depth datasets are collected

These datasets are typically collected using stereo cameras, where two or more cameras placed at fixed distances capture images simultaneously from slightly different perspectives, allowing for depth information extraction. The NYU-Depth V2 dataset uses RGB-D cameras that capture depth values along with pixel colors. Some datasets utilize LiDAR, projecting laser beams to capture 3D information about a scene.

However, these methods come with several problems. The amount of labeled data is limited due to the high operational costs of obtaining these datasets. Additionally, the annotations can be noisy and low-resolution. Stereo cameras struggle under various lighting conditions and can’t reliably identify transparent or highly reflective surfaces. LiDAR is expensive, and both LiDAR and RGB-D cameras have limited range and generate low-resolution, sparse depth maps.

Can we use Unlabelled Images to learn Depth Estimation?

It would be beneficial to use unlabeled images to train depth estimation models, given the abundance of such images available online. The major innovation proposed in the original Depth Anything paper from 2023 was the incorporation of these unlabeled datasets into the training pipeline. In the next section, we’ll explore how this was achieved.

READ ALSO

The 5-Second Fingerprint: Inside Shazam’s Prompt Tune ID

STOP Constructing Ineffective ML Initiatives – What Really Works


How do neural networks be taught to estimate depth from 2D photos?

Avishek Biswas

Towards Data Science

10 min learn

·

14 hours in the past

What’s Monocular Depth Estimation?

The Depth Something V2 Algorithm (Illustration by Writer)

Monocular Depth Estimation (MDE) is the duty of coaching a neural community to find out depth data from a single picture. That is an thrilling and difficult space of Machine Studying and Laptop Imaginative and prescient as a result of predicting a depth map requires the neural community to type a three-d understanding from only a 2-dimensional picture.

On this article, we’ll talk about a brand new mannequin known as Depth Something V2 and its precursor, Depth Something V1. Depth Something V2 has outperformed almost all different fashions in Depth Estimation, exhibiting spectacular outcomes on difficult photos.

Depth Something V2 Demo (Supply: Display recording by the writer from Depth Something V2 DEMO web page)

This text relies on a video I made on the identical subject. Here’s a video hyperlink for learners preferring a visible medium. For individuals who want studying, proceed!

Why ought to we even care about MDE fashions?

Good MDE fashions have many sensible makes use of, akin to aiding navigation and impediment avoidance for robots, drones, and autonomous automobiles. They may also be utilized in video and picture enhancing, background alternative, object elimination, and creating 3D results. Moreover, they’re helpful for AR and VR headsets to create interactive 3D areas across the consumer.

There are two predominant approaches for doing MDE (this text solely covers one)

Two predominant approaches have emerged for coaching MDE fashions — one, discriminative approaches the place the community tries to foretell depth as a supervised studying goal, and two, generative approaches like conditional diffusion the place depth prediction is an iterative picture era process. Depth Something belongs to the primary class of discriminative approaches, and that’s what we might be discussing right now. Welcome to Neural Breakdown, and let’s go deep with Depth Estimation[!

To fully understand Depth Anything, let’s first revisit the MiDAS paper from 2019, which serves as a precursor to the Depth Anything algorithm.

Source: Screenshot taken from the MIDAS Paper (License: Free)

MiDAS trains an MDE model using a combination of different datasets containing labeled depth information. For instance, the KITTI dataset for autonomous driving provides outdoor images, while the NYU-Depth V2 dataset offers indoor scenes. Understanding how these datasets are collected is crucial because newer models like Depth Anything and Depth Anything V2 address several issues inherent in the data collection process.

How real-world depth datasets are collected

These datasets are typically collected using stereo cameras, where two or more cameras placed at fixed distances capture images simultaneously from slightly different perspectives, allowing for depth information extraction. The NYU-Depth V2 dataset uses RGB-D cameras that capture depth values along with pixel colors. Some datasets utilize LiDAR, projecting laser beams to capture 3D information about a scene.

However, these methods come with several problems. The amount of labeled data is limited due to the high operational costs of obtaining these datasets. Additionally, the annotations can be noisy and low-resolution. Stereo cameras struggle under various lighting conditions and can’t reliably identify transparent or highly reflective surfaces. LiDAR is expensive, and both LiDAR and RGB-D cameras have limited range and generate low-resolution, sparse depth maps.

Can we use Unlabelled Images to learn Depth Estimation?

It would be beneficial to use unlabeled images to train depth estimation models, given the abundance of such images available online. The major innovation proposed in the original Depth Anything paper from 2023 was the incorporation of these unlabeled datasets into the training pipeline. In the next section, we’ll explore how this was achieved.

Tags: AvishekBiswasDepthEstimationJulMonocular

Related Posts

1dv5wrccnuvdzg6fvwvtnuq@2x.jpg
Artificial Intelligence

The 5-Second Fingerprint: Inside Shazam’s Prompt Tune ID

July 8, 2025
0 dq7oeogcaqjjio62.jpg
Artificial Intelligence

STOP Constructing Ineffective ML Initiatives – What Really Works

July 7, 2025
2025 06 30 22 56 21 ezgif.com video to gif converter.gif
Artificial Intelligence

Interactive Knowledge Exploration for Laptop Imaginative and prescient Tasks with Rerun

July 6, 2025
Rulefit 1024x683.png
Artificial Intelligence

Explainable Anomaly Detection with RuleFit: An Intuitive Information

July 6, 2025
Lineage graph.jpg
Artificial Intelligence

Change-Conscious Knowledge Validation with Column-Stage Lineage

July 5, 2025
Ai interview 1024x683.png
Artificial Intelligence

Rethinking Knowledge Science Interviews within the Age of AI

July 4, 2025
Next Post
Generative ai a way of life 01.webp.webp

Pioneering the Way forward for Innovation

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

POPULAR NEWS

0 3.png

College endowments be a part of crypto rush, boosting meme cash like Meme Index

February 10, 2025
Gemini 2.0 Fash Vs Gpt 4o.webp.webp

Gemini 2.0 Flash vs GPT 4o: Which is Higher?

January 19, 2025
1da3lz S3h Cujupuolbtvw.png

Scaling Statistics: Incremental Customary Deviation in SQL with dbt | by Yuval Gorchover | Jan, 2025

January 2, 2025
0khns0 Djocjfzxyr.jpeg

Constructing Data Graphs with LLM Graph Transformer | by Tomaz Bratanic | Nov, 2024

November 5, 2024
How To Maintain Data Quality In The Supply Chain Feature.jpg

Find out how to Preserve Knowledge High quality within the Provide Chain

September 8, 2024

EDITOR'S PICK

Growtika Ngocbxiaro0 Unsplash.jpg

Will Qu?antum Computer systems Outpace Our Means to Safe Knowledge

September 28, 2024
Tucker Carlson Calls For Better Crypto Usability.webp.webp

Bitcoin Should Be Simpler for Monetary Freedom

October 30, 2024
3f36da8e 0f0c 4d45 a562 c9813346018d 800x420.jpg

Michael Saylor pitches Technique’s Bitcoin credit score mannequin to Trump’s FHFA Director

June 24, 2025
Shutterstock Copyright Symbol.jpg

Examine suggests OpenAI is not ready for copyright exemption • The Register

April 3, 2025

About Us

Welcome to News AI World, your go-to source for the latest in artificial intelligence news and developments. Our mission is to deliver comprehensive and insightful coverage of the rapidly evolving AI landscape, keeping you informed about breakthroughs, trends, and the transformative impact of AI technologies across industries.

Categories

  • Artificial Intelligence
  • ChatGPT
  • Crypto Coins
  • Data Science
  • Machine Learning

Recent Posts

  • Run Your Python Code as much as 80x Sooner Utilizing the Cython Library
  • CRO Surges After Fact Social’s Crypto Blue-Chip ETF Disclosure, XRP Underperforms
  • IBM’s Breakthrough: Quantum Leap or Quantum Hype?
  • Home
  • About Us
  • Contact Us
  • Disclaimer
  • Privacy Policy

© 2024 Newsaiworld.com. All rights reserved.

No Result
View All Result
  • Home
  • Artificial Intelligence
  • ChatGPT
  • Data Science
  • Machine Learning
  • Crypto Coins
  • Contact Us

© 2024 Newsaiworld.com. All rights reserved.

Are you sure want to unlock this post?
Unlock left : 0
Are you sure want to cancel subscription?