UCTNet: Uncertainty-Aware Cross-Modal Transformer Network for Indoor RGB-D Semantic Segmentation

In this paper, we tackle the problem of RGB-D Semantic Segmentation. The key challenges in solving this problem lie in 1) how to extract features from depth sensor data and 2) how to effectively fuse the features extracted from the two modalities. For the first challenge, we found that the depth information obtained from the sensor is not always reliable (e.g. objects with reflective or dark surfaces typically have inaccurate or void sensor readings), and existing methods that extract depth features using ConvNets did not explicitly consider the reliability of depth value at different pixel locations. To tackle this challenge, we propose a novel mechanism, namely Uncertainty-Aware Self-Attention that explicitly controls the information flow from unreliable depth pixels to confident depth pixels during feature extraction. For the second challenge, we propose an effective and scalable fusion module based on Cross-Attention that can perform adaptive and asymmetric information exchange between the RGB and depth encoder. Our proposed framework, namely UCTNet, is an encoder-decoder network that naturally incorporates these two key designs for robust and accurate RGB-D Segmentation. Experimental results show that UCTNet outperforms existing works and achieves state-of-the-art performances on two RGB-D Semantic Segmentation benchmarks.