Multimodal fusion can make semantic segmentation more robust. However, fusing an
arbitrary number of modalities remains underexplored. To delve into this problem, we create …

Building general-purpose models that can perceive diverse real-world modalities and solve
various tasks is an appealing target in artificial intelligence. In this paper, we present …

3D segmentation is a fundamental and challenging problem in computer vision with
applications in autonomous driving and robotics. It has received significant attention from the …

The rapid progression of convolutional neural networks (CNNs) has significantly improved
indoor scene parsing, transforming the fields of robotics, autonomous navigation …

RGB-D semantic segmentation is crucial for robots to understand scenes. Most existing
methods take depth information as an additional input, leading to cross-modal semantic …

Multi-sensor clues have shown promise for object segmentation, but inherent noise in each
sensor, as well as the calibration error in practice, may bias the segmentation accuracy. In …

Complementarily fusing RGB and depth images while effectively suppressing task-irrelevant
noise is crucial for achieving accurate indoor RGB-D semantic segmentation. In this paper …

Semantic segmentation of high-resolution multispectral remote sensing image has been
intensely studied. However, the shadow occlusions, or the similar color and textures …

Abstract 3D segmentation is a fundamental and challenging problem in computer vision with
applications in autonomous driving and robotics. It has received significant attention from the …

Scene understanding in a production workshop is an important technology to improve its
intelligence level, semantic segmentation of production workshop objects is an effective …