Living Scenes: Multi-object Relocalization and Reconstruction in Changing 3D Environments
arxiv(2023)
摘要
Research into dynamic 3D scene understanding has primarily focused on
short-term change tracking from dense observations, while little attention has
been paid to long-term changes with sparse observations. We address this gap
with MoRE, a novel approach for multi-object relocalization and reconstruction
in evolving environments. We view these environments as "living scenes" and
consider the problem of transforming scans taken at different points in time
into a 3D reconstruction of the object instances, whose accuracy and
completeness increase over time. At the core of our method lies an
SE(3)-equivariant representation in a single encoder-decoder network, trained
on synthetic data. This representation enables us to seamlessly tackle instance
matching, registration, and reconstruction. We also introduce a joint
optimization algorithm that facilitates the accumulation of point clouds
originating from the same instance across multiple scans taken at different
points in time. We validate our method on synthetic and real-world data and
demonstrate state-of-the-art performance in both end-to-end performance and
individual subtasks.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要