Depth Anything 3: Recovering the Visual Space from Any Views
Haotong Lin et al.
arXiv:2511.10647
In short
Depth Anything 3 recovers consistent 3D geometry and camera poses from any number of images, with or without known camera positions. It uses a plain transformer backbone and a single depth-and-ray prediction target, trained teacher-to-student on public data.
Why it matters
A simpler recipe beats specialised 3D pipelines, suggesting 3D perception can ride on general vision backbones.
Read first
The 4 Field Guide ideas this paper leans on.
Starting from scratch? The full route 18 ideas · basics first
- Machine Learning ✓ understood
Building systems that learn patterns from data instead of following hand-written rules, getting better at a task as they see more examples.
- Neural Network ✓ understood
A computational model inspired by biological neural networks, consisting of interconnected nodes (neurons) organized in layers that process information through weighted connections.
- Deep Learning ✓ understood
A subset of machine learning that uses neural networks with multiple layers (deep neural networks) to learn hierarchical representations of data.
- Computer Vision ✓ understood
The field of AI that gets computers to extract meaning from images and video: what is in them, where it is, and how it moves.
- Dataset ✓ understood
A collection of data examples used for training, validating, or testing machine learning models.
- Feature ✓ understood
A single measurable property of an example, such as a house's floor area or how many links an email contains, used as an input to a model.
- Representation Learning ✓ understood
Learning useful features or representations of data automatically, rather than hand-crafting them.
- Embedding ✓ understood
A list of numbers (a vector) that represents a word, sentence, image or other item, learned so that similar items end up close together.
- Attention Mechanism ✓ understood
A technique that lets a neural network weigh every part of its input when producing each output, focusing on the parts most relevant at that step.
- Transformer ✓ understood
A neural network architecture, introduced in 2017, built from stacked self-attention and feed-forward layers; the basis of nearly every modern large language model.
- Vision Transformer · read first ✓ understood
Applying the transformer architecture to computer vision by treating image patches as tokens, achieving state-of-the-art results.
- Depth Estimation · read first ✓ understood
Predicting distance of objects from the camera using monocular or stereo images.
- 3D Reconstruction · read first ✓ understood
Creating 3D models from 2D images using geometry and deep learning.
- Training ✓ understood
The process of fitting a model to data by repeatedly measuring how wrong its outputs are and adjusting its parameters to reduce that error.
- Activation Function ✓ understood
A non-linear function applied to neuron outputs that introduces non-linearity, enabling networks to learn complex patterns.
- Softmax ✓ understood
A function that turns a list of scores (logits) into probabilities that are all positive and sum to 1; the standard output of classifiers and language models.
- Knowledge Distillation ✓ understood
Training a smaller 'student' model to mimic a larger 'teacher' model, transferring knowledge while reducing size.
- Teacher Model · read first ✓ understood
The larger, more accurate model in knowledge distillation that guides student training.
In the frontier
- Rank
- #7 of 100
- Citations
- 499
- as of Aug 9, 2026
- Published
- Nov 2025
Topics: 3D and spatial intelligence , Vision-language models , Video generation and world models
Selection: 1kpapers.com by Together AI, most-cited as of Aug 9, 2026