LiDAR point cloud analysis is a core task for 3D computer vision, especially for autonomous
driving. However, due to the severe sparsity and noise interference in the single sweep …

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 …

The joint training framework for speech enhancement and recognition methods have
obtained quite good performances for robust end-to-end automatic speech recognition …

Abstract Semantic Scene Completion (SSC) transforms an image of single-view depth
and/or RGB 2D pixels into 3D voxels, each of whose semantic labels are predicted. SSC is a …

We study a new problem of semantic complete scene forecasting (SCSF) in this work. Given
a 4D dynamic point cloud sequence, our goal is to forecast the complete scene …

Semantic scene completion (SSC) jointly predicts the semantics and geometry of the entire
3D scene, which plays an essential role in 3D scene understanding for autonomous driving …

Semantic scene completion (SSC) requires the estimation of the 3D geometric occupancies
of objects in the scene, along with the object categories. Currently, many methods employ …

Semantic scene completion (SSC) aims to predict complete 3D voxel occupancy and
semantics from a single-view RGB-D image and recent SSC methods commonly adopt multi …

Semantic Scene Completion (SSC) aims to reconstruct complete 3D scenes with precise
voxel-wise semantics from the single-view incomplete input data, a crucial but highly …