We propose a learning-based method to reconstruct the local terrain for
locomotion with a mobile robot traversing urban environments. Using a stream of
depth measurements from the onboard cameras and the robot's trajectory, the
algorithm estimates the topography in the robot's vicinity. The raw
measurements from these cameras are noisy and only provide partial and occluded
observations that in many cases do not show the terrain the robot stands on.
Therefore, we propose a 3D reconstruction model that faithfully reconstructs
the scene, despite the noisy measurements and large amounts of missing data
coming from the blind spots of the camera arrangement. The model consists of a
4D fully convolutional network on point clouds that learns the geometric priors
to complete the scene from the context and an auto-regressive feedback to
leverage spatio-temporal consistency and use evidence from the past. The
network can be solely trained with synthetic data, and due to extensive
augmentation, it is robust in the real world, as shown in the validation on a
quadrupedal robot, ANYmal, traversing challenging settings. We run the pipeline
on the robot's onboard low-power computer using an efficient sparse tensor
implementation and show that the proposed method outperforms classical map
representations.

本文提出一种基于学习的方法，重建移动机器人行进城市环境中的局部地形，通过算法估计机器人周围的地形，采用 4D 全卷积网络，可以利用合成数据进行训练，并在机器人上取得良好效果。

结构化地形运动的神经场景表征

Neural Scene Representation for Locomotion on Structured Terrain

State-of-the-art approaches to infer dense depth measurements from images
rely on CNNs trained end-to-end on a vast amount of data. However, these
approaches suffer a drastic drop in accuracy when dealing with environments
much different in appearance and/or context from those observed at training
time. This domain shift issue is usually addressed by fine-tuning on smaller
sets of images from the target domain annotated with depth labels.
Unfortunately, relying on such supervised labeling is seldom feasible in most
practical settings. Therefore, we propose an unsupervised domain adaptation
technique which does not require groundtruth labels. Our method relies only on
image pairs and leverages on classical stereo algorithms to produce disparity
measurements alongside with confidence estimators to assess upon their
reliability. We propose to fine-tune both depth-from-stereo as well as
depth-from-mono architectures by a novel confidence-guided loss function that
handles the measured disparities as noisy labels weighted according to the
estimated confidence. Extensive experimental results based on standard datasets
and evaluation protocols prove that our technique can address effectively the
domain shift issue with both stereo and monocular depth prediction
architectures and outperforms other state-of-the-art unsupervised loss
functions that may be alternatively deployed to pursue domain adaptation.

本文介绍了一种无监督领域适应技术，它仅依赖于图像对，并利用经典的立体算法生成视差测量，旨在 fine-tune 立体和单眼深度架构，通过一种新颖的置信度引导损失函数来处理以测量出视差值，并且该方法不需要基础真实标签，实验证明其效果优于其他同类无监督损失函数。

图像深度预测的无监督领域适应

Unsupervised Domain Adaptation for Depth Prediction from Images

We introduce DIODE, a dataset that contains thousands of diverse high
resolution color images with accurate, dense, long-range depth measurements.
DIODE (Dense Indoor/Outdoor DEpth) is the first public dataset to include RGBD
images of indoor and outdoor scenes obtained with one sensor suite. This is in
contrast to existing datasets that focus on just one domain/scene type and
employ different sensors, making generalization across domains difficult. The
dataset is available for download at this http URL

DIODE 数据集包含数千个具有精确、密集、远距离深度测量的不同高分辨率彩色图像，是第一个包含使用单个传感器套件获得的室内和室外场景的 RGBD 图像的公开数据集。

DIODE: 一个密集的户内外深度数据集

DIODE: A Dense Indoor and Outdoor DEpth Dataset

Obtaining accurate depth measurements out of a single image represents a
fascinating solution to 3D sensing. CNNs led to considerable improvements in
this field, and recent trends replaced the need for ground-truth labels with
geometry-guided image reconstruction signals enabling unsupervised training.
Currently, for this purpose, state-of-the-art techniques rely on images
acquired with a binocular stereo rig to predict inverse depth (i.e., disparity)
according to the aforementioned supervision principle. However, these methods
suffer from well-known problems near occlusions, left image border, etc
inherited from the stereo setup. Therefore, in this paper, we tackle these
issues by moving to a trinocular domain for training. Assuming the central
image as the reference, we train a CNN to infer disparity representations
pairing such image with frames on its left and right side. This strategy allows
obtaining depth maps not affected by typical stereo artifacts. Moreover, being
trinocular datasets seldom available, we introduce a novel interleaved training
procedure enabling to enforce the trinocular assumption outlined from current
binocular datasets. Exhaustive experimental results on the KITTI dataset
confirm that our proposal outperforms state-of-the-art methods for unsupervised
monocular depth estimation trained on binocular stereo pairs as well as any
known methods relying on other cues.

该论文提出了一种可以使用无标注数据进行训练的，基于三目摄像机的深度估计方法，其在 KITTI 数据集上取得了比其他基于双目摄像机以及其他线索的方法更好的性能。