For the last few decades, several major subfields of artificial intelligence including computer
vision, graphics, and robotics have progressed largely independently from each other …

Semantic scene understanding is important for various applications. In particular, self-driving
cars need a fine-grained understanding of the surfaces and objects in their vicinity. Light …

A central goal of visual recognition is to understand objects and scenes from a single image.
2D recognition has witnessed tremendous progress thanks to large-scale learning and …

Recent deep networks that directly handle points in a point set, eg, PointNet, have been
state-of-the-art for supervised learning tasks on point clouds such as classification and …

This paper focuses on semantic scene completion, a task for producing a complete 3D voxel
representation of volumetric occupancy and semantic labels for a scene from a single-view …

Superpixels group perceptually similar pixels to create visually meaningful entities while
heavily reducing the number of primitives for subsequent processing steps. As of these …

We introduce a new approach for multiscale 3Dsemantic scene completion from voxelized
sparse 3D LiDAR scans. As opposed to the literature, we use a 2D UNet backbone with …

Semantic scene completion (SSC) aims to jointly estimate the complete geometry and
semantics of a scene, assuming partial sparse input. In the last years following the …

With the increasing reliance of self-driving and similar robotic systems on robust 3D vision,
the processing of LiDAR scans with deep convolutional neural networks has become a trend …

The goal of the Semantic Scene Completion (SSC) task is to simultaneously predict a
completed 3D voxel representation of volumetric occupancy and semantic labels of objects …