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?

- The Definitive Guide to Building RAG Apps with LlamaIndex
- Accelerated Vector Search
- The Definitive Guide to Building RAG Apps with LangChain
- Advanced Techniques in Vector Database Management
- Getting Started with Zilliz Cloud
- 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
What’s the recommended error-handling strategy for Gemini 3 tool calls?
A good error-handling strategy for Gemini 3 tool calls starts with assuming that both the model and the tools can fail i
What is distributed AI in multi-agent systems?
Distributed AI in multi-agent systems refers to the approach where multiple autonomous agents collaborate, communicate,
How is perplexity used to measure LLM performance?
Perplexity is a metric used to evaluate how well an LLM predicts a sequence of tokens. It quantifies the uncertainty of