3D computer vision involves using algorithms and systems to interpret and understand three-dimensional structures from visual data like images or videos. It enables machines to analyze depth, shape, and spatial relationships in a scene. Applications range from object recognition to 3D scene reconstruction. A key aspect is depth estimation, which determines the distance of objects from the camera. Techniques include stereo vision, which uses two camera views, and depth sensors like LiDAR. These methods are essential for creating 3D models of environments in applications like robotics and augmented reality. Another critical use is motion analysis. By tracking objects in 3D space, systems like self-driving cars can make real-time decisions based on their surroundings. 3D computer vision also supports virtual reality by enabling realistic scene rendering and interaction.
What is 3D computer vision?
- Exploring Vector Database Use Cases
- How to Pick the Right Vector Database for Your Use Case
- Embedding 101
- Natural Language Processing (NLP) Basics
- Getting Started with Milvus
- All learn series →
Recommended AI Learn Series
VectorDB for GenAI Apps
Zilliz Cloud is a managed vector database perfect for building GenAI applications.
Try Zilliz Cloud for FreeKeep Reading
In the context of RAG, what does the term “answer correctness” specifically entail, and how can it be measured differently from generic text similarity?
**Answer Correctness in RAG**
In Retrieval-Augmented Generation (RAG), "answer correctness" refers to whether the gener
How are vectors generated from data?
Vectors are generated from data through a process known as embedding. This involves converting raw data, such as text or
How does data migration work in SaaS?
Data migration in SaaS (Software as a Service) involves transferring data from one system to another, usually during the