In the past few years, we have seen great progress in perception algorithms,
particular through the use of deep learning. However, most existing approaches
focus on a few categories of interest, which represent only a small fraction of
the potential categories that robots need to handle in the real-world. Thus,
identifying objects from unknown classes remains a challenging yet crucial
task. In this paper, we develop a novel open-set instance segmentation
algorithm for point clouds which can segment objects from both known and
unknown classes in a holistic way. Our method uses a deep convolutional neural
network to project points into a category-agnostic embedding space in which
they can be clustered into instances irrespective of their semantics.
Experiments on two large-scale self-driving datasets validate the effectiveness
of our proposed method.

本文提出了一种基于深度学习的开放式场景下点云实例分割算法，能够从点云场景中分割出不同类别的实例。实验在两个自动驾驶数据集上验证了该方法的有效性。

自动驾驶车辆中未知实例的识别

Identifying Unknown Instances for Autonomous Driving

In this paper, we tackle the problem of relational behavior forecasting from
sensor data. Towards this goal, we propose a novel spatially-aware graph neural
network (SpAGNN) that models the interactions between agents in the scene.
Specifically, we exploit a convolutional neural network to detect the actors
and compute their initial states. A graph neural network then iteratively
updates the actor states via a message passing process. Inspired by Gaussian
belief propagation, we design the messages to be spatially-transformed
parameters of the output distributions from neighboring agents. Our model is
fully differentiable, thus enabling end-to-end training. Importantly, our
probabilistic predictions can model uncertainty at the trajectory level. We
demonstrate the effectiveness of our approach by achieving significant
improvements over the state-of-the-art on two real-world self-driving datasets:
ATG4D and nuScenes.

本文提出了一种基于空间感知图神经网络的关系行为预测模型，该模型能够建模场景中的交互，并在轨迹级别上模拟不确定性，通过最新的自动驾驶数据集 ATG4D 和 nuScenes 的实验结果，我们的模型取得了显著的性能提升，具有端到端的训练能力。