Accurately perceiving and tracking instances over time is essential for the
decision-making processes of autonomous agents interacting safely in dynamic
environments. With this intention, we propose Mask4D for the challenging task
of 4D panoptic segmentation of LiDAR point clouds. Mask4D is the first
transformer-based approach unifying semantic instance segmentation and tracking
of sparse and irregular sequences of 3D point clouds into a single joint model.
Our model directly predicts semantic instances and their temporal associations
without relying on any hand-crafted non-learned association strategies such as
probabilistic clustering or voting-based center prediction. Instead, Mask4D
introduces spatio-temporal instance queries which encode the semantic and
geometric properties of each semantic tracklet in the sequence. In an in-depth
study, we find that it is critical to promote spatially compact instance
predictions as spatio-temporal instance queries tend to merge multiple
semantically similar instances, even if they are spatially distant. To this
end, we regress 6-DOF bounding box parameters from spatio-temporal instance
queries, which is used as an auxiliary task to foster spatially compact
predictions. Mask4D achieves a new state-of-the-art on the SemanticKITTI test
set with a score of 68.4 LSTQ, improving upon published top-performing methods
by at least +4.5%.

Mask4D 是一种基于 Transformer 的方法，用于将 LiDAR 点云进行 4D 全景分割的挑战性任务，直接预测语义实例及其时间关联，无需借助任何手工设计的非学习关联策略，通过引入时空实例查询编码每个实例轨迹的语义和几何特性，并从时空实例查询回归 6DOF 边界框参数以促进紧凑的空间预测，最终在 SemanticKITTI 测试集上取得了 68.4 LSTQ 的新的最优效果，相较于已发表的表现最佳方法提高了至少 4.5%。

MASK4D：4D 全景分割的掩码变换器

MASK4D: Mask Transformer for 4D Panoptic Segmentation

Recent deep learning models achieve impressive results on 3D scene analysis
tasks by operating directly on unstructured point clouds. A lot of progress was
made in the field of object classification and semantic segmentation. However,
the task of instance segmentation is less explored. In this work, we present
3D-BEVIS, a deep learning framework for 3D semantic instance segmentation on
point clouds. Following the idea of previous proposal-free instance
segmentation approaches, our model learns a feature embedding and groups the
obtained feature space into semantic instances. Current point-based methods
scale linearly with the number of points by processing local sub-parts of a
scene individually. However, to perform instance segmentation by clustering,
globally consistent features are required. Therefore, we propose to combine
local point geometry with global context information from an intermediate
bird's-eye view representation.

本文介绍了一种基于点云的深度学习框架 3D-BEVIS，旨在对 3D 场景进行语义实例分割，通过将局部点几何和中间俯视图表示的全局环境信息相结合，以聚类的方式学习特征嵌入并将其分组成语义实例。