Depth-aware CNN for RGB-D Segmentation (1803.06791v1)

Abstract: Convolutional neural networks (CNN) are limited by the lack of capability to handle geometric information due to the fixed grid kernel structure. The availability of depth data enables progress in RGB-D semantic segmentation with CNNs. State-of-the-art methods either use depth as additional images or process spatial information in 3D volumes or point clouds. These methods suffer from high computation and memory cost. To address these issues, we present Depth-aware CNN by introducing two intuitive, flexible and effective operations: depth-aware convolution and depth-aware average pooling. By leveraging depth similarity between pixels in the process of information propagation, geometry is seamlessly incorporated into CNN. Without introducing any additional parameters, both operators can be easily integrated into existing CNNs. Extensive experiments and ablation studies on challenging RGB-D semantic segmentation benchmarks validate the effectiveness and flexibility of our approach.

• The paper presents a novel depth-aware CNN that integrates specialized depth convolution and pooling to effectively utilize geometric information in RGB-D segmentation.
• The depth-aware convolution emphasizes pixels with similar depth to the kernel center, preserving object boundaries and improving segmentation accuracy.
• Experimental results on NYUv2, SUN-RGBD, and SID benchmarks show superior mean IoU performance without increasing network complexity.

Depth-aware CNN for RGB-D Segmentation: An Overview

The paper "Depth-aware CNN for RGB-D Segmentation" by Weiyue Wang and Ulrich Neumann addresses a notable limitation in conventional convolutional neural networks (CNNs) with regard to handling geometric information inherent in depth data. Traditional CNNs leverage fixed grid-based convolutional operations, which are not suited to model geometric variations present in depth images. This limitation restricts their efficacy in RGB-D semantic segmentation tasks, where depth data can provide valuable geometric context complementing the RGB images.

Key Contributions

The authors propose an innovative approach via the Depth-aware CNN (D-CNN) framework, which integrates two novel operations into the CNN architecture: depth-aware convolution and depth-aware average pooling. These operations are designed to judiciously incorporate geometric relationships derived from depth data.

1. Depth-aware Convolution: This operation augments standard convolution by adding a depth similarity term. The pixels whose depths are similar to the center of a convolutional kernel are given more weight during the convolution operation, thereby emphasizing depth-consistent information propagation.
2. Depth-aware Average Pooling: In this operation, the pooling process considers the depth similarity between pixels within the local neighborhood, which helps in preserving object boundaries and preventing boundary blurring that standard average pooling may cause.

These operators efficiently leverage geometric properties without introducing additional parameters, maintaining the computational efficiency of traditional CNNs while significantly enhancing the geometric understanding of the framework.

Experimental Evaluation

The proposed D-CNN is rigorously evaluated on several challenging RGB-D semantic segmentation benchmarks, namely NYUv2, SUN-RGBD, and Stanford Indoor Dataset (SID). The results consistently demonstrate that D-CNN outperforms traditional CNN baselines on these datasets. Notably, the network's architecture allows it to outperform methods like the HHA approach, which doubles network complexity by processing depth and RGB channels separately.

• On the NYUv2 dataset, the D-CNN achieved a mean IoU of 41.0%, outperforming several competing methods.
• In the SUN-RGBD dataset, the D-CNN's mean IoU of 29.7% highlights its effective handling of depth information over conventional baselines.
• The SID dataset results corroborate that D-CNN achieves superior IoU scores while maintaining the same level of computational cost.

Impact and Future Directions

The paper provides a foundation for integrating depth information into existing 2D CNN architectures efficiently, with significant implications for a range of vision tasks requiring RGB-D data processing. The D-CNN framework bridges the gap between 2D CNN paradigms and 3D geometric understanding, offering a powerful tool for tasks such as object detection, instance segmentation, and real-time semantic mapping.

Future research could explore extending the D-CNN framework to more complex 3D data types such as LiDAR point clouds. Additionally, adapting depth-aware operations for more extensive architectures or applying them in real-time environments could provide further insights into their practical applicability and versatility in diverse conditions.

In conclusion, this paper enriches the field of RGB-D semantic segmentation by presenting a coherent approach to leveraging depth in CNNs, promising further advancements in geometry-aware intelligence systems.