We propose a novel unsupervised method to learn the pose and
part-segmentation of articulated objects with rigid parts. Given two
observations of an object in different articulation states, our method learns
the geometry and appearance of object parts by using an implicit model from the
first observation, distils the part segmentation and articulation from the
second observation while rendering the latter observation. Additionally, to
tackle the complexities in the joint optimization of part segmentation and
articulation, we propose a voxel grid-based initialization strategy and a
decoupled optimization procedure. Compared to the prior unsupervised work, our
model obtains significantly better performance, and generalizes to objects with
multiple parts while it can be efficiently from few views for the latter
observation.

通过使用第一次观测的隐式模型学习对象的几何和外观部分，并通过渲染第二次观测来蒸馏部分分割和关节运动，我们提出了一种新的无监督方法来学习刚性部分组成的关节对象的姿势和部分分割。此外，为了解决部分分割和关节运动的联合优化中的复杂性，我们提出了基于体素网格的初始化策略和解耦优化过程。与先前的无监督方法相比，我们的模型性能显著提高，且适用于具有多个部分的对象，且对后续观测可以高效地进行少数视图的操作。

通过有条件的视图合成实现无监督的关节物体建模

Articulate your NeRF: Unsupervised articulated object modeling via  conditional view synthesis

We present a novel approach to learning a point-wise, meaningful embedding
for point-clouds in an unsupervised manner, through the use of neural-networks.
The domain of point-cloud processing via neural-networks is rapidly evolving,
with novel architectures and applications frequently emerging. Within this
field of research, the availability and plethora of unlabeled point-clouds as
well as their possible applications make finding ways of characterizing this
type of data appealing. Though significant advancement was achieved in the
realm of unsupervised learning, its adaptation to the point-cloud
representation is not trivial. Previous research focuses on the embedding of
entire point-clouds representing an object in a meaningful manner. We present a
deep learning framework to learn point-wise description from a set of shapes
without supervision. Our approach leverages self-supervision to define a
relevant loss function to learn rich per-point features. We train a
neural-network with objectives based on context derived directly from the raw
data, with no added annotation. We use local structures of point-clouds to
incorporate geometric information into each point's latent representation. In
addition to using local geometric information, we encourage adjacent points to
have similar representations and vice-versa, creating a smoother, more
descriptive representation. We demonstrate the ability of our method to capture
meaningful point-wise features through three applications. By clustering the
learned embedding space, we perform unsupervised part-segmentation on point
clouds. By calculating euclidean distance in the latent space we derive
semantic point-analogies. Finally, by retrieving nearest-neighbors in our
learned latent space we present meaningful point-correspondence within and
among point-clouds.

本文提出了一种无监督学习点云的有意义的点对点嵌入方法，利用神经网络的自我监督来训练，从而实现对无标签数据进行语义分割。