With the recent growth of urban mapping and autonomous driving efforts, there
has been an explosion of raw 3D data collected from terrestrial platforms with
lidar scanners and color cameras. However, due to high labeling costs,
ground-truth 3D semantic segmentation annotations are limited in both quantity
and geographic diversity, while also being difficult to transfer across
sensors. In contrast, large image collections with ground-truth semantic
segmentations are readily available for diverse sets of scenes. In this paper,
we investigate how to use only those labeled 2D image collections to supervise
training 3D semantic segmentation models. Our approach is to train a 3D model
from pseudo-labels derived from 2D semantic image segmentations using multiview
fusion. We address several novel issues with this approach, including how to
select trusted pseudo-labels, how to sample 3D scenes with rare object
categories, and how to decouple input features from 2D images from
pseudo-labels during training. The proposed network architecture, 2D3DNet,
achieves significantly better performance (+6.2-11.4 mIoU) than baselines
during experiments on a new urban dataset with lidar and images captured in 20
cities across 5 continents.

本文提出了一种基于 2D 图像标注来训练 3D 语义分割模型的方法，使用多视角融合来生成伪标签，并解决了选择可信的伪标签、较少目标分类、在训练过程中如何脱离 2D 图像等问题。实验结果表明，所提出的 2D3DNet 网络在一个新的城市数据集上的表现显著优于基线方法。

只利用 2D 图像监督学习 3D 语义分割

Learning 3D Semantic Segmentation with only 2D Image Supervision

Occlusion is probably the biggest challenge for human pose estimation in the
wild. Typical solutions often rely on intrusive sensors such as IMUs to detect
occluded joints. To make the task truly unconstrained, we present AdaFuse, an
adaptive multiview fusion method, which can enhance the features in occluded
views by leveraging those in visible views. The core of AdaFuse is to determine
the point-point correspondence between two views which we solve effectively by
exploring the sparsity of the heatmap representation. We also learn an adaptive
fusion weight for each camera view to reflect its feature quality in order to
reduce the chance that good features are undesirably corrupted by ``bad''
views. The fusion model is trained end-to-end with the pose estimation network,
and can be directly applied to new camera configurations without additional
adaptation. We extensively evaluate the approach on three public datasets
including Human3.6M, Total Capture and CMU Panoptic. It outperforms the
state-of-the-arts on all of them. We also create a large scale synthetic
dataset Occlusion-Person, which allows us to perform numerical evaluation on
the occluded joints, as it provides occlusion labels for every joint in the
images. The dataset and code are released at
this https URL

提出一种自适应多视角融合方法 AdaFuse 来提高 Human Pose Estimation 在遮挡背景下的准确性，其核心内容在于通过热图的稀疏性探索两个视图之间的点对应关系和学习每个摄像头视图的自适应融合权重，在公共数据集和大规模合成数据集上得到了比其他方法更优的结果。