Depth completion is a crucial task in autonomous driving, aiming to convert a
sparse depth map into a dense depth prediction. Due to its potentially rich
semantic information, RGB image is commonly fused to enhance the completion
effect. Image-guided depth completion involves three key challenges: 1) how to
effectively fuse the two modalities; 2) how to better recover depth
information; and 3) how to achieve real-time prediction for practical
autonomous driving. To solve the above problems, we propose a concise but
effective network, named CENet, to achieve high-performance depth completion
with a simple and elegant structure. Firstly, we use a fast guidance module to
fuse the two sensor features, utilizing abundant auxiliary features extracted
from the color space. Unlike other commonly used complicated guidance modules,
our approach is intuitive and low-cost. In addition, we find and analyze the
optimization inconsistency problem for observed and unobserved positions, and a
decoupled depth prediction head is proposed to alleviate the issue. The
proposed decoupled head can better output the depth of valid and invalid
positions with very few extra inference time. Based on the simple structure of
dual-encoder and single-decoder, our CENet can achieve superior balance between
accuracy and efficiency. In the KITTI depth completion benchmark, our CENet
attains competitive performance and inference speed compared with the
state-of-the-art methods. To validate the generalization of our method, we also
evaluate on indoor NYUv2 dataset, and our CENet still achieve impressive
results. The code of this work will be available at
this https URL

深度补全是自动驾驶中关键的任务之一，旨在将稀疏的深度图转化为密集的深度预测。通过基于 RGB 图像的融合，我们提出了一种简洁而有效的网络结构 CENet，实现了高性能深度补全，具有优秀的准确性和效率。

自动驾驶中基于图像导向的深度补全的简明有效网络

A Concise but Effective Network for Image Guided Depth Completion in  Autonomous Driving

Accurate and fast scene understanding is one of the challenging task for
autonomous driving, which requires to take full advantage of LiDAR point clouds
for semantic segmentation. In this paper, we present a \textbf{concise} and
\textbf{efficient} image-based semantic segmentation network, named
\textbf{CENet}. In order to improve the descriptive power of learned features
and reduce the computational as well as time complexity, our CENet integrates
the convolution with larger kernel size instead of MLP, carefully-selected
activation functions, and multiple auxiliary segmentation heads with
corresponding loss functions into architecture. Quantitative and qualitative
experiments conducted on publicly available benchmarks, SemanticKITTI and
SemanticPOSS, demonstrate that our pipeline achieves much better mIoU and
inference performance compared with state-of-the-art models. The code will be
available at this https URL

本文介绍了一种名为 CENet 的图像语义分割网络，采用卷积而非 MLP，精选的激活函数和多个辅助分割头来提高特征描述能力和降低计算时间复杂度，使用公开的基准测试 SemanticKITTI 和 SemanticPOSS 进行的实验表明，与现有模型相比，我们的模型在 mIoU 和推理性能方面均表现更好。