Learn depth space from light field via a distance-constraint query mechanism

The Light Field (LF) captures both spatial and angular information of scenes, enabling precise depth estimation. Recent advancements in deep learning have led to significant success in this field; however, existing methods primarily focus on modeling surface characteristics (e.g., depth maps) while overlooking the depth space, which contains additional valuable information. The depth space consists of numerous space points and provides substantially more geometric data than a single depth map. In this paper, we conceptualize depth prediction as a spatial modeling problem, aiming to learn the entire depth space rather than merely a single depth map. Specifically, we define space points as signed distances relative to the scene surface and propose a novel distance-constraint query mechanism for LF depth estimation. To model the depth space effectively, we first develop a mixed sampling strategy to approximate its data representation. Subsequently, we introduce an encoder-decoder network architecture to query the distances of each point, thereby implicitly embedding the depth space. Finally, to extract the target depth map from this space, we present a generation algorithm that iteratively invokes the decoder network. Through extensive experiments, our approach achieves the highest performance on LF depth estimation benchmarks, and also demonstrates superior performance on various synthetic and real-world scenes.