论文标题
3DDEPTHNET：点云引导深度完成网络，用于稀疏深度和单色图像
3dDepthNet: Point Cloud Guided Depth Completion Network for Sparse Depth and Single Color Image
论文作者
论文摘要
在本文中，我们提出了一个名为3DDEPTHNET的端到端深度学习网络，该网络可从一对稀疏的激光雷达深度和彩色图像中产生准确的密集深度图像，用于机器人和自主驾驶任务。基于深度图像的维度性质，我们的网络提供了一种新颖的3D至2D粗到1点双重致密设计，既准确又轻量级。深度致密化首先是通过Point Cloud完成在3D空间中执行的，然后是专门设计的编码器码头结构，该结构利用了从3D完成和原始RGB-D图像进行投影的密度深度来执行2D图像完成。 KITTI数据集的实验显示我们的网络达到了最先进的精度，同时又提高了效率。消融和概括测试证明，我们网络中的每个模块都会对最终结果产生积极影响，此外，我们的网络对于更稀疏的深度具有弹性。
In this paper, we propose an end-to-end deep learning network named 3dDepthNet, which produces an accurate dense depth image from a single pair of sparse LiDAR depth and color image for robotics and autonomous driving tasks. Based on the dimensional nature of depth images, our network offers a novel 3D-to-2D coarse-to-fine dual densification design that is both accurate and lightweight. Depth densification is first performed in 3D space via point cloud completion, followed by a specially designed encoder-decoder structure that utilizes the projected dense depth from 3D completion and the original RGB-D images to perform 2D image completion. Experiments on the KITTI dataset show our network achieves state-of-art accuracy while being more efficient. Ablation and generalization tests prove that each module in our network has positive influences on the final results, and furthermore, our network is resilient to even sparser depth.
