• Home
  • About Us
  • Contact Us
  • Disclaimer
  • Privacy Policy
Saturday, July 12, 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

Hitchhiker’s Information to RAG: From Tiny Information to Tolstoy with OpenAI’s API and LangChain

Scene Understanding in Motion: Actual-World Validation of Multimodal AI Integration


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

Data mining 3 hanna barakat aixdesign archival images of ai 3328x2312.png
Artificial Intelligence

Hitchhiker’s Information to RAG: From Tiny Information to Tolstoy with OpenAI’s API and LangChain

July 12, 2025
Chapter3 cover image capture.png
Artificial Intelligence

Scene Understanding in Motion: Actual-World Validation of Multimodal AI Integration

July 11, 2025
Intro image 683x1024.png
Artificial Intelligence

Lowering Time to Worth for Knowledge Science Tasks: Half 3

July 10, 2025
Drawing 22 scaled 1.png
Artificial Intelligence

Work Information Is the Subsequent Frontier for GenAI

July 10, 2025
Grpo4.png
Artificial Intelligence

How one can Superb-Tune Small Language Fashions to Suppose with Reinforcement Studying

July 9, 2025
Gradio.jpg
Artificial Intelligence

Construct Interactive Machine Studying Apps with Gradio

July 8, 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

Bonk.webp.webp

BONK Types Double Backside, Targets $0.000019 Breakout

April 17, 2025
Exphormer2005large.gif

Scaling transformers for graph-structured knowledge

August 18, 2024
1oyom vjg1dl28nmiejfasa.png

Let’s reproduce NanoGPT with JAX!(Half 1) | by Louis Wang | Jul, 2024

August 4, 2024
Ripple Ceo Brad On Crypto Role In Us Politics Xrp Future.webp.webp

Ripple CEO on Crypto’s Function in US Politics & XRP’s Imaginative and prescient

December 9, 2024

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

  • Hitchhiker’s Information to RAG: From Tiny Information to Tolstoy with OpenAI’s API and LangChain
  • Are You Being Unfair to LLMs?
  • Robinhood Provides Crypto Buying and selling “on the Lowest Price,” however Is It False Promoting?
  • 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?