Continuous Depth Representation

Continuous depth representation in computer vision aims to move beyond discrete depth values, enabling more accurate and detailed 3D scene understanding. Current research focuses on developing methods that learn continuous depth maps directly, often employing clustering techniques, large-scale datasets, and novel architectures like transformers to improve accuracy and robustness, particularly at object boundaries and in handling noisy data. This improved representation is crucial for various applications, including depth completion, monocular depth estimation, and downstream tasks like object detection and autonomous driving, leading to more reliable and precise 3D perception.