EdgeNet: Semantic Scene Completion from a Single RGB-D Image
Channel: Teo de Campos
Date: 08/01/2021

Presentation given at ICPR 2020. Further information and source code is available from https://cic.unb.br/~teodecampos/aloisio/

Given an RGB-D image, the goal of semantic scene completion is to infer a complete 3D occupancy grid with associated semantic labels.
Previous works completely neglect the RGB channels from the input data or require a complex two step training process to merge RGB and depth data.
Our Edge-Net representation encodes colour information in 3D space using edge detection and flipped truncated signed distance (F-TSDF), which improves semantic completion scores especially in hard to detect classes, with an end-to-end 3D deep neural network.