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?

- Accelerated Vector Search
- Information Retrieval 101
- GenAI Ecosystem
- Mastering Audio AI
- Natural Language Processing (NLP) Advanced Guide
- 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 you design the neural network for the reverse diffusion step?
Designing a neural network for the reverse diffusion step involves creating an architecture that effectively learns how
What is quantum supremacy, and has it been achieved yet?
Quantum supremacy refers to the point at which a quantum computer can perform a calculation that is practically impossib
How does speaker adaptation work in TTS?
Speaker adaptation in text-to-speech (TTS) systems adjusts a pre-trained model to mimic a specific speaker’s voice witho