How does the inference latency of the proposed DDRNet23-slim variant compare to baseline segmentation models o

Abstract

Abstract: Point clouds provide a flexible geometric representation suitable for countless applications in computer graphics; they also comprise the raw output of most 3D data acquisition devices. While hand-designed features on point clouds have long been proposed in graphics and vision, however, the recent overwhelming success of convolutional neural networks (CNNs) for image analysis suggests the value of adapting insight from CNN to the point cloud world. Point clouds inherently lack topological information, so designing a model to recover topology can enrich the representation power of point clouds.

Research Question

How does the inference latency of the proposed DDRNet23-slim variant compare to baseline segmentation models on the RUGD dataset when evaluated using mean intersection over union (mIoU) and frames per second (FPS) metrics?

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