Estimating the depth of objects from a single image is a valuable task for
many vision, robotics, and graphics applications. However, current methods
often fail to produce accurate depth for objects in diverse scenes. In this
work, we propose a simple yet effective Background Prompting strategy that
adapts the input object image with a learned background. We learn the
background prompts only using small-scale synthetic object datasets. To infer
object depth on a real image, we place the segmented object into the learned
background prompt and run off-the-shelf depth networks. Background Prompting
helps the depth networks focus on the foreground object, as they are made
invariant to background variations. Moreover, Background Prompting minimizes
the domain gap between synthetic and real object images, leading to better
sim2real generalization than simple finetuning. Results on multiple synthetic
and real datasets demonstrate consistent improvements in real object depths for
a variety of existing depth networks. Code and optimized background prompts can
be found at: this https URL

本研究提出一种简单而有效的背景提示策略，利用学习到的背景适应输入的物体图像，改善现有深度估计方法难以应对多样性场景的问题，并通过缩小合成和真实对象图像之间的领域差距来实现较好的从合成到真实世界的泛化性能。