This article presents a GPU-accelerated software design of the recently proposed model of Slanted Stixels, which represents the geometric and semantic information of a scene in a compact and accurate way. We reformulate the measurement depth model to reduce the computational complexity of the algorithm, relying on the confidence of the depth estimation and the identification of invalid values to handle outliers. The proposed massively parallel scheme and data layout for the irregular computation pattern that corresponds to a Dynamic Programming paradigm is described and carefully analyzed in performance terms. Performance is shown to scale gracefully on current generation embedded GPUs. We assess the proposed methods in terms of semantic and geometric accuracy as well as run-time performance on three publicly available benchmark datasets. Our approach achieves real-time performance with high accuracy for 2048 × 1024 image sizes and 4 × 4 Stixel resolution on the low-power embedded GPU of an NVIDIA Tegra Xavier.

中文翻译：

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在GPU上具有倾斜式Stixels的3D感知

本文介绍了最近提出的Slanted Stixels模型的GPU加速软件设计，该模型以紧凑而准确的方式表示场景的几何和语义信息。我们依靠深度估计的置信度和无效值的标识来处理异常值，从而重新构造测量深度模型以降低算法的计算复杂性。描述并针对性能规划对与动态编程范例相对应的不规则计算模式提出的大规模并行方案和数据布局进行了详细分析。在当前的嵌入式GPU上，性能可以正常扩展。我们在语义和几何精度以及三个公开基准数据集的运行时性能方面评估了所提出的方法。