In this paper we present a scalable approach for robustly computing a 3D
surface mesh from multi-scale multi-view stereo point clouds that can handle
extreme jumps of point density (in our experiments three orders of magnitude).
The backbone of our approach is a combination of octree data partitioning,
local Delaunay tetrahedralization and graph cut optimization. Graph cut
optimization is used twice, once to extract surface hypotheses from local
Delaunay tetrahedralizations and once to merge overlapping surface hypotheses
even when the local tetrahedralizations do not share the same topology.This
formulation allows us to obtain a constant memory consumption per sub-problem
while at the same time retaining the density independent interpolation
properties of the Delaunay-based optimization. On multiple public datasets, we
demonstrate that our approach is highly competitive with the state-of-the-art
in terms of accuracy, completeness and outlier resilience. Further, we
demonstrate the multi-scale potential of our approach by processing a newly
recorded dataset with 2 billion points and a point density variation of more
than four orders of magnitude - requiring less than 9GB of RAM per process.

本文提出一种可扩展且健壮的方法，通过多视点三维立体点云计算三维表面网格，能够处理点密度极大跳跃，用于提取表面假设和合并表面假设，具有卓越的精度和完整性。

具有极端规模和密度多样性的点云可扩展表面重建

Scalable Surface Reconstruction from Point Clouds with Extreme Scale and  Density Diversity

Interactive object selection is a very important research problem and has
many applications. Previous algorithms require substantial user interactions to
estimate the foreground and background distributions. In this paper, we present
a novel deep learning based algorithm which has a much better understanding of
objectness and thus can reduce user interactions to just a few clicks. Our
algorithm transforms user provided positive and negative clicks into two
Euclidean distance maps which are then concatenated with the RGB channels of
images to compose (image, user interactions) pairs. We generate many of such
pairs by combining several random sampling strategies to model user click
patterns and use them to fine tune deep Fully Convolutional Networks (FCNs).
Finally the output probability maps of our FCN 8s model is integrated with
graph cut optimization to refine the boundary segments. Our model is trained on
the PASCAL segmentation dataset and evaluated on other datasets with different
object classes. Experimental results on both seen and unseen objects clearly
demonstrate that our algorithm has a good generalization ability and is
superior to all existing interactive object selection approaches.

本研究介绍一种新颖的基于深度学习的算法来解决交互式物体选择的问题，该算法可以将用户提供的正负反馈转换成欧几里得距离图，并与图像的 RGB 通道拼接，采用多种随机采样策略生成图像和用户交互对，通过 Fine-tune 深度反卷积网络，最终结果融合图像概率图和图切割优化，实现对物体边界的精细提取。实验结果表明，该算法具有较好的泛化能力，优于所有现有的交互式物体选择方法。