Monocular depth inference is a fundamental problem for scene perception of
robots. Specific robots may be equipped with a camera plus an optional depth
sensor of any type and located in various scenes of different scales, whereas
recent advances derived multiple individual sub-tasks. It leads to additional
burdens to fine-tune models for specific robots and thereby high-cost
customization in large-scale industrialization. This paper investigates a
unified task of monocular depth inference, which infers high-quality depth maps
from all kinds of input raw data from various robots in unseen scenes. A basic
benchmark G2-MonoDepth is developed for this task, which comprises four
components: (a) a unified data representation RGB+X to accommodate RGB plus raw
depth with diverse scene scale/semantics, depth sparsity ([0%, 100%]) and
errors (holes/noises/blurs), (b) a novel unified loss to adapt to diverse depth
sparsity/errors of input raw data and diverse scales of output scenes, (c) an
improved network to well propagate diverse scene scales from input to output,
and (d) a data augmentation pipeline to simulate all types of real artifacts in
raw depth maps for training. G2-MonoDepth is applied in three sub-tasks
including depth estimation, depth completion with different sparsity, and depth
enhancement in unseen scenes, and it always outperforms SOTA baselines on both
real-world data and synthetic data.

该论文研究了单目深度推断的统一任务，从各种机器人的不同场景中的各种原始数据中推断出高质量的深度图。开发了基本的基准测试 G2-MonoDepth，应用于深度估计、不同稀疏度的深度完成和未见场景中的深度增强，并在真实数据和合成数据上始终优于最先进的基准模型。