Sparse and Dense Data with CNNs: Depth Completion and Semantic Segmentation

• Published in: arXiv.org
• Main Authors: Jaritz, Maximilian, de Charette, Raoul, Wirbel, Emilie, Perrotton, Xavier, Nashashibi, Fawzi
• Format: Paper
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 31.08.2018
• Subjects: Artificial neural networks; Lidar; Neural networks; Semantic segmentation; Semantics

Convolutional neural networks are designed for dense data, but vision data is often sparse (stereo depth, point clouds, pen stroke, etc.). We present a method to handle sparse depth data with optional dense RGB, and accomplish depth completion and semantic segmentation changing only the last layer....