In autonomous driving, 3D occupancy prediction outputs voxel-wise status and
semantic labels for more comprehensive understandings of 3D scenes compared
with traditional perception tasks, such as 3D object detection and bird's-eye
view (BEV) semantic segmentation. Recent researchers have extensively explored
various aspects of this task, including view transformation techniques,
ground-truth label generation, and elaborate network design, aiming to achieve
superior performance. However, the inference speed, crucial for running on an
autonomous vehicle, is neglected. To this end, a new method, dubbed FastOcc, is
proposed. By carefully analyzing the network effect and latency from four
parts, including the input image resolution, image backbone, view
transformation, and occupancy prediction head, it is found that the occupancy
prediction head holds considerable potential for accelerating the model while
keeping its accuracy. Targeted at improving this component, the time-consuming
3D convolution network is replaced with a novel residual-like architecture,
where features are mainly digested by a lightweight 2D BEV convolution network
and compensated by integrating the 3D voxel features interpolated from the
original image features. Experiments on the Occ3D-nuScenes benchmark
demonstrate that our FastOcc achieves state-of-the-art results with a fast
inference speed.

在自动驾驶中，通过分析网络效应和延迟，本文提出了一种名为 FastOcc 的新方法，通过用轻量级的 2D BEV 卷积网络代替时间消耗较大的 3D 卷积网络，加快模型的推理速度，同时保持准确性，从而实现了优越的性能。

FastOcc: 通过融合二维俯视图和透视视图加速三维占据预测

FastOcc: Accelerating 3D Occupancy Prediction by Fusing the 2D  Bird's-Eye View and Perspective View

In this paper, we propose a conceptually novel, efficient, and fully
convolutional framework for real-time instance segmentation. Previously, most
instance segmentation methods heavily rely on object detection and perform mask
prediction based on bounding boxes or dense centers. In contrast, we propose a
sparse set of instance activation maps, as a new object representation, to
highlight informative regions for each foreground object. Then instance-level
features are obtained by aggregating features according to the highlighted
regions for recognition and segmentation. Moreover, based on bipartite
matching, the instance activation maps can predict objects in a one-to-one
style, thus avoiding non-maximum suppression (NMS) in post-processing. Owing to
the simple yet effective designs with instance activation maps, SparseInst has
extremely fast inference speed and achieves 40 FPS and 37.9 AP on the COCO
benchmark, which significantly outperforms the counterparts in terms of speed
and accuracy. Code and models are available at
this https URL

本文提出了一种实时实例分割的概念性新颖、高效并且完全卷积的框架。该框架采用了一种新的目标表示方法 —— 稀疏的实例激活图，通过聚合特征识别和分割前景对象，在一对一的模式下预测物体，避免后处理中的非极大值抑制。该方法在 COCO 基准测试中实现了 40 FPS 和 37.9 AP，速度和准确性均得到显著提高。