In this paper, we present a method for dynamic surface reconstruction of
large-scale urban scenes from LiDAR. Depth-based reconstructions tend to focus
on small-scale objects or large-scale SLAM reconstructions that treat moving
objects as outliers. We take a holistic perspective and optimize a
compositional model of a dynamic scene that decomposes the world into rigidly
moving objects and the background. To achieve this, we take inspiration from
recent novel view synthesis methods and pose the reconstruction problem as a
global optimization, minimizing the distance between our predicted surface and
the input LiDAR scans. We show how this global optimization can be decomposed
into registration and surface reconstruction steps, which are handled well by
off-the-shelf methods without any re-training. By careful modeling of
continuous-time motion, our reconstructions can compensate for the rolling
shutter effects of rotating LiDAR sensors. This allows for the first system (to
our knowledge) that properly motion compensates LiDAR scans for rigidly-moving
objects, complementing widely-used techniques for motion compensation of static
scenes. Beyond pursuing dynamic reconstruction as a goal in and of itself, we
also show that such a system can be used to auto-label partially annotated
sequences and produce ground truth annotation for hard-to-label problems such
as depth completion and scene flow.

本文提出了一种用于从 LiDAR 重建大规模城市场景的动态表面重建方法，通过全局优化降低预测表面与输入 LiDAR 扫描数据的距离，实现了对刚性移动物体的运动补偿，并展示了该系统在自动标记局部注释序列和生成难以标记问题的真实注释方面的应用。

同时进行地图和物体重建

Simultaneous Map and Object Reconstruction

We introduce Motion2VecSets, a 4D diffusion model for dynamic surface
reconstruction from point cloud sequences. While existing state-of-the-art
methods have demonstrated success in reconstructing non-rigid objects using
neural field representations, conventional feed-forward networks encounter
challenges with ambiguous observations from noisy, partial, or sparse point
clouds. To address these challenges, we introduce a diffusion model that
explicitly learns the shape and motion distribution of non-rigid objects
through an iterative denoising process of compressed latent representations.
The diffusion-based prior enables more plausible and probabilistic
reconstructions when handling ambiguous inputs. We parameterize 4D dynamics
with latent vector sets instead of using a global latent. This novel 4D
representation allows us to learn local surface shape and deformation patterns,
leading to more accurate non-linear motion capture and significantly improving
generalizability to unseen motions and identities. For more temporal-coherent
object tracking, we synchronously denoise deformation latent sets and exchange
information across multiple frames. To avoid the computational overhead, we
design an interleaved space and time attention block to alternately aggregate
deformation latents along spatial and temporal domains. Extensive comparisons
against the state-of-the-art methods demonstrate the superiority of our
Motion2VecSets in 4D reconstruction from various imperfect observations,
notably achieving a 19% improvement in Intersection over Union (IoU) compared
to CaDex for reconstructing unseen individuals from sparse point clouds on the
DeformingThings4D-Animals dataset. More detailed information can be found at
this https URL

我们介绍了 Motion2VecSets，这是一种用于动态表面重建的四维扩散模型，通过点云序列。我们通过迭代去噪的压缩潜在表示过程，明确学习非刚性物体的形状和运动分布，从而使得在处理模糊的输入时，扩散先验模型能够实现更可信和概率化的重建效果。