Depth Estimation using Custom CNN (Encoder-Decoder)

This project presents a custom Convolutional Neural Network (CNN) designed for monocular depth estimation using an encoder–decoder architecture. The model learns to predict depth maps from single RGB images, capturing spatial structure and scene geometry.

Model Architecture (Encoder-Decoder)

Future Improvements

• Use pretrained backbone (e.g., MobileNet, ResNet)
• Add attention mechanisms
• Optimize for Edge AI deployment
• Convert to ONNX / TensorRT for faster inference

Contributing

Contributions are welcome! Feel free to open issues or submit pull requests.