We address the problem of building digital twins of unknown articulated
objects from two RGBD scans of the object at different articulation states. We
decompose the problem into two stages, each addressing distinct aspects. Our
method first reconstructs object-level shape at each state, then recovers the
underlying articulation model including part segmentation and joint
articulations that associate the two states. By explicitly modeling point-level
correspondences and exploiting cues from images, 3D reconstructions, and
kinematics, our method yields more accurate and stable results compared to
prior work. It also handles more than one movable part and does not rely on any
object shape or structure priors. Project page:
this https URL

我们通过两个不同关节状态的物体的两个 RGBD 扫描构建未知关节物体的数字孪生问题，通过分解成两个阶段并采用显式建模，我们的方法能够重建物体形状并恢复关节模型，通过利用图像、3D 重建和运动学信息，我们的方法相比之前的工作具有更高的准确性和稳定性，并且能够处理多个活动部件并不依赖于任何物体形状或结构的先验知识。

未知关节物体的数字双胞胎建模的神经隐式表示

Neural Implicit Representation for Building Digital Twins of Unknown  Articulated Objects

This paper proposes a new approach for automated floorplan reconstruction
from RGBD scans, a major milestone in indoor mapping research. The approach,
dubbed Floor-SP, formulates a novel optimization problem, where room-wise
coordinate descent sequentially solves dynamic programming to optimize the
floorplan graph structure. The objective function consists of data terms guided
by deep neural networks, consistency terms encouraging adjacent rooms to share
corners and walls, and the model complexity term. The approach does not require
corner/edge detection with thresholds, unlike most other methods. We have
evaluated our system on production-quality RGBD scans of 527 apartments or
houses, including many units with non-Manhattan structures. Qualitative and
quantitative evaluations demonstrate a significant performance boost over the
current state-of-the-art. Please refer to our project website
this http URL for code and data.

本文提出了一种新的自动化平面图重建方法，该方法由名为 Floor-SP 的优化问题构成，其中每个房间的坐标下降顺序地解决动态规划以优化楼层图结构，该方法不需要阈值的角点 / 边缘检测，重点在数据项上，引导深度神经网络生成建筑物模型，并通过一系列一致性和模型复杂度项来优化。评估结果表明，该方法比当前最先进的方法具有显著的性能改进