Analyzing scenes thoroughly is crucial for mobile robots acting in different
environments. Semantic segmentation can enhance various subsequent tasks, such
as (semantically assisted) person perception, (semantic) free space detection,
(semantic) mapping, and (semantic) navigation. In this paper, we propose an
efficient and robust RGB-D segmentation approach that can be optimized to a
high degree using NVIDIA TensorRT and, thus, is well suited as a common initial
processing step in a complex system for scene analysis on mobile robots. We
show that RGB-D segmentation is superior to processing RGB images solely and
that it can still be performed in real time if the network architecture is
carefully designed. We evaluate our proposed Efficient Scene Analysis Network
(ESANet) on the common indoor datasets NYUv2 and SUNRGB-D and show that we
reach state-of-the-art performance while enabling faster inference.
Furthermore, our evaluation on the outdoor dataset Cityscapes shows that our
approach is suitable for other areas of application as well. Finally, instead
of presenting benchmark results only, we also show qualitative results in one
of our indoor application scenarios.

本文介绍了一种使用 NVIDIA TensorRT 进行优化的高效且稳健的 RGB-D 分割方法，该方法可作为场景分析系统的公共初始处理步骤。我们通过使用 ESANet 在 NYUv2 和 SUNRGB-D 室内数据集上进行评估，展示了 RGB-D 分割优于仅处理 RGB 图像的效果，并证明当网络架构精心设计时仍可实时完成。此外，我们在 Cityscapes 的室外数据集上进行评估，显示了我们的方法也适用于其他应用领域，并展示了我们在一个室内应用场景中的定性结果.