Shape assembly aims to reassemble parts (or fragments) into a complete
object, which is a common task in our daily life. Different from the semantic
part assembly (e.g., assembling a chair's semantic parts like legs into a whole
chair), geometric part assembly (e.g., assembling bowl fragments into a
complete bowl) is an emerging task in computer vision and robotics. Instead of
semantic information, this task focuses on geometric information of parts. As
the both geometric and pose space of fractured parts are exceptionally large,
shape pose disentanglement of part representations is beneficial to geometric
shape assembly. In our paper, we propose to leverage SE(3) equivariance for
such shape pose disentanglement. Moreover, while previous works in vision and
robotics only consider SE(3) equivariance for the representations of single
objects, we move a step forward and propose leveraging SE(3) equivariance for
representations considering multi-part correlations, which further boosts the
performance of the multi-part assembly. Experiments demonstrate the
significance of SE(3) equivariance and our proposed method for geometric shape
assembly. Project page: this https URL

我们提出利用 SE (3) 等变性对形状姿态进行解耦，并在多部分协作方面提供了改进，这大大提高了几何形状装配的性能。

利用 SE（3）等变性学习 3D 几何形状装配

Leveraging SE(3) Equivariance for Learning 3D Geometric Shape Assembly

Human-designed visual manuals are crucial components in shape assembly
activities. They provide step-by-step guidance on how we should move and
connect different parts in a convenient and physically-realizable way. While
there has been an ongoing effort in building agents that perform assembly
tasks, the information in human-design manuals has been largely overlooked. We
identify that this is due to 1) a lack of realistic 3D assembly objects that
have paired manuals and 2) the difficulty of extracting structured information
from purely image-based manuals. Motivated by this observation, we present
IKEA-Manual, a dataset consisting of 102 IKEA objects paired with assembly
manuals. We provide fine-grained annotations on the IKEA objects and assembly
manuals, including decomposed assembly parts, assembly plans, manual
segmentation, and 2D-3D correspondence between 3D parts and visual manuals. We
illustrate the broad application of our dataset on four tasks related to shape
assembly: assembly plan generation, part segmentation, pose estimation, and 3D
part assembly.

本文介绍了 IKEA-Manual 数据集，提供了 102 个 IKEA 物体的图形化组装手册，包含细致全面的注释，以解决目前在构建能够执行装配任务的代理时，忽略了人类设计手册的信息的问题。该数据集有助于提高组装计划生成、部件分割、姿态估计和 3D 部件组装的效率。

IKEA-Manual: 逐步看清造型组装

IKEA-Manual: Seeing Shape Assembly Step by Step

We introduce Breaking Bad, a large-scale dataset of fractured objects. Our
dataset consists of over one million fractured objects simulated from ten
thousand base models. The fracture simulation is powered by a recent physically
based algorithm that efficiently generates a variety of fracture modes of an
object. Existing shape assembly datasets decompose objects according to
semantically meaningful parts, effectively modeling the construction process.
In contrast, Breaking Bad models the destruction process of how a geometric
object naturally breaks into fragments. Our dataset serves as a benchmark that
enables the study of fractured object reassembly and presents new challenges
for geometric shape understanding. We analyze our dataset with several geometry
measurements and benchmark three state-of-the-art shape assembly deep learning
methods under various settings. Extensive experimental results demonstrate the
difficulty of our dataset, calling on future research in model designs
specifically for the geometric shape assembly task. We host our dataset at
this https URL

本研究介绍了 Breaking Bad 数据集，它包含来自一万个基本模型的超过一百万个被模拟的碎裂物体，并通过最新的基于物理的算法实现碎裂模拟，为几何形状理解和重新组装碎裂物体提供了新的挑战。

破裂与装配几何数据集

Breaking Bad: A Dataset for Geometric Fracture and Reassembly

Assembling parts into an object is a combinatorial problem that arises in a
variety of contexts in the real world and involves numerous applications in
science and engineering. Previous related work tackles limited cases with
identical unit parts or jigsaw-style parts of textured shapes, which greatly
mitigate combinatorial challenges of the problem. In this work, we introduce
the more challenging problem of shape assembly, which involves textureless
fragments of arbitrary shapes with indistinctive junctions, and then propose a
learning-based approach to solving it. We demonstrate the effectiveness on
shape assembly tasks with various scenarios, including the ones with abnormal
fragments (e.g., missing and distorted), the different number of fragments, and
different rotation discretization.

这篇研究论文提出了一种基于学习的方法，用于解决组装任意形状的无纹理碎片的问题，并展示了该方法在不同情境下的有效性。