In this work, we propose \textit{MVFuseNet}, a novel end-to-end method for
joint object detection and motion forecasting from a temporal sequence of LiDAR
data. Most existing methods operate in a single view by projecting data in
either range view (RV) or bird's eye view (BEV). In contrast, we propose a
method that effectively utilizes both RV and BEV for spatio-temporal feature
learning as part of a temporal fusion network as well as for multi-scale
feature learning in the backbone network. Further, we propose a novel
sequential fusion approach that effectively utilizes multiple views in the
temporal fusion network. We show the benefits of our multi-view approach for
the tasks of detection and motion forecasting on two large-scale self-driving
data sets, achieving state-of-the-art results. Furthermore, we show that
MVFusenet scales well to large operating ranges while maintaining real-time
performance.

提出了一种名为 MVFuseNet 的方法，在利用脉冲激光雷达（LiDAR）数据时，通过多视图联合学习以及多尺度特征提取的方式，实现了目标检测和运动预测，取得了最新的技术成果，并且验证了该方法在大范围的自动驾驶数据集上可以很好地拓展并保持实时性能。

MVFuseNet: 通过多视角融合 LiDAR 数据，提高端到端对象检测和运动预测的性能

MVFuseNet: Improving End-to-End Object Detection and Motion Forecasting  through Multi-View Fusion of LiDAR Data

We introduce a new neural architecture and an unsupervised algorithm for
learning invariant representations from temporal sequence of images. The system
uses two groups of complex cells whose outputs are combined multiplicatively:
one that represents the content of the image, constrained to be constant over
several consecutive frames, and one that represents the precise location of
features, which is allowed to vary over time but constrained to be sparse. The
architecture uses an encoder to extract features, and a decoder to reconstruct
the input from the features. The method was applied to patches extracted from
consecutive movie frames and produces orientation and frequency selective units
analogous to the complex cells in V1. An extension of the method is proposed to
train a network composed of units with local receptive field spread over a
large image of arbitrary size. A layer of complex cells, subject to sparsity
constraints, pool feature units over overlapping local neighborhoods, which
causes the feature units to organize themselves into pinwheel patterns of
orientation-selective receptive fields, similar to those observed in the
mammalian visual cortex. A feed-forward encoder efficiently computes the
feature representation of full images.

介绍一个新的神经网络架构和无监督算法，用于学习从时间序列图像中获得不变表示，该系统使用两组复杂细胞，一个表示图像的内容，另一个表示精确的位置特征，并且使用一个编码器提取特征。