With the invention of the low-cost Microsoft Kinect sensor, high-resolution depth and visual
(RGB) sensing has become available for widespread use. The complementary nature of the …

Semantic segmentation, also called scene labeling, refers to the process of assigning a
semantic label (eg car, people, and road) to each pixel of an image. It is an essential data …

Access to large, diverse RGB-D datasets is critical for training RGB-D scene understanding
algorithms. However, existing datasets still cover only a limited number of views or a …

Pixel-wise semantic segmentation for visual scene understanding not only needs to be
accurate, but also efficient in order to find any use in real-time application. Existing …

A key requirement for leveraging supervised deep learning methods is the availability of
large, labeled datasets. Unfortunately, in the context of RGB-D scene understanding, very …

Recently, very deep convolutional neural networks (CNNs) have shown outstanding
performance in object recognition and have also been the first choice for dense …

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 …

This paper addresses the problem of estimating the depth map of a scene given a single
RGB image. We propose a fully convolutional architecture, encompassing residual learning …

In this paper, we propose a method for semantic parsing the 3D point cloud of an entire
building using a hierarchical approach: first, the raw data is parsed into semantically …

We present a novel and practical deep fully convolutional neural network architecture for
semantic pixel-wise segmentation termed SegNet. This core trainable segmentation engine …