This paper studies statistical characteristics of multivariate observations with irregular changes in their covariance structures across input space.

Then, to recover the features of missed voxels due to incorrect voxel-wise segmentation, we build afully sparse convolutional RoI pooling module todirectly aggregate fine-grained spatial information from backbone for further proposal refinement. It is memory-and-computation efficient and can better encode the geometry-specific featuresofeach3Dproposal.

Neural Information Processing Systems http://nips.cc/

PAnoramic Semantic Segmentation (PASS) isanimportant task incomputer vision, as it enables semantic understanding of a 360 environment. Currently, most of existing works have focused on addressing the distortion issues in 2D panoramic images without considering spatial properties of indoor scene. This restricts PASS methods inperceiving contextual attributestodealwith theambiguity when working with monocular images. In this paper, we propose anovel approach for indoor panoramic semantic segmentation. Unlike previous works, we consider the panoramic image as a composition of segment groups:oversampled segments,representing planar structures suchasfloorsandceilings, and under-sampled segments, representing other scene elements.

Information (IGN) that provides a unique and rich resource for large-scale geospa-tial analysis.