Depth Synthesis

Depth synthesis focuses on generating realistic depth maps from various inputs, such as RGB images, semantic layouts, or sparse depth data, aiming to bridge the gap between simulated and real-world data for applications like robotics and computer vision. Current research emphasizes developing novel architectures, including GANs and transformers, to improve the accuracy and efficiency of depth map generation, often incorporating techniques like self-supervised learning and multi-view stereo to leverage multiple perspectives and enhance geometric consistency. These advancements are crucial for improving the performance of downstream tasks such as scene graph generation, 3D reconstruction, and RGB-D object recognition, ultimately enabling more robust and reliable AI systems in real-world environments.