Tutorials on RGB-D (color and depth) image segmentation can be found on platforms like Medium, YouTube, and GitHub. Specific resources include research-oriented blogs on Towards Data Science and video tutorials on channels like StatQuest or Deeplearning.ai. Framework documentation, such as PyTorch and TensorFlow, often includes examples of semantic segmentation that can be adapted for RGB-D data. For advanced learners, papers with code repositories (https://paperswithcode.com/) provide cutting-edge implementations. Exploring datasets like NYU Depth V2 or SUN RGB-D will also help you practice and apply segmentation techniques.
Where can I find tutorials about RGB-D image segmentation?

- Master Video AI
- Optimizing Your RAG Applications: Strategies and Methods
- How to Pick the Right Vector Database for Your Use Case
- Advanced Techniques in Vector Database Management
- Natural Language Processing (NLP) Basics
- 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
How do AI agents handle conflicting input data?
AI agents manage conflicting input data using a combination of techniques such as data prioritization, context-based rea
What is the role of domain expertise in choosing a dataset?
Domain expertise plays a crucial role in choosing the right dataset for any project, particularly in fields like machine
How does big data handle global data distribution?
Big data handles global data distribution through the use of distributed computing systems, which allow data to be proce