RedNet: Residual Encoder-Decoder Network for indoor RGB-D Semantic Segmentation (1806.01054v2)

Abstract: Indoor semantic segmentation has always been a difficult task in computer vision. In this paper, we propose an RGB-D residual encoder-decoder architecture, named RedNet, for indoor RGB-D semantic segmentation. In RedNet, the residual module is applied to both the encoder and decoder as the basic building block, and the skip-connection is used to bypass the spatial feature between the encoder and decoder. In order to incorporate the depth information of the scene, a fusion structure is constructed, which makes inference on RGB image and depth image separately, and fuses their features over several layers. In order to efficiently optimize the network's parameters, we propose a `pyramid supervision' training scheme, which applies supervised learning over different layers in the decoder, to cope with the problem of gradients vanishing. Experiment results show that the proposed RedNet(ResNet-50) achieves a state-of-the-art mIoU accuracy of 47.8% on the SUN RGB-D benchmark dataset.

• The paper introduces a dual-branch residual encoder-decoder that fuses RGB and depth data for accurate indoor semantic segmentation.
• The approach employs pyramid supervision to mitigate gradient vanishing, achieving a notable mIoU of 47.8% on the SUN RGB-D benchmark.
• The design leverages skip connections and residual blocks to maintain spatial fidelity and enhance feature fusion in challenging indoor scenes.

Review of RedNet: Residual Encoder-Decoder Network for Indoor RGB-D Semantic Segmentation

The paper introduces a novel approach, RedNet, aimed at addressing challenges in indoor semantic segmentation using RGB-D data. Indoor semantic segmentation remains a complex task due to the subtle differences in color and structure among indoor objects and non-uniform lighting conditions. RedNet is designed to harness both color and depth information efficiently by integrating a residual encoder-decoder architecture, which aligns with contemporary trends in convolutional neural networks (CNNs) and deep learning for semantic segmentation.

RedNet employs a dual-branch encoder with residual learning blocks, drawing from the ResNet family, to process RGB and depth data separately before fusing their features. In doing so, it captures more expressive features that benefit from depth information complementing the RGB input. The decoder mirrors the encoder's architecture by utilizing symmetric residual blocks, while incorporating skip connections to maintain spatial fidelity from the encoding phase through to the semantic predictions. This design choice is strongly motivated by the need to address issues prevalent in traditional architectures, such as vanishing gradients and spatial resolution loss.

The introduction of a 'pyramid supervision' training regime constitutes a notable contribution. By applying supervised learning across different layers within the decoder, the approach mitigates gradient vanishing—a longstanding issue in deep network optimization. This multifaceted supervision ensures that diverse levels of abstraction are simultaneously refined during the training process, progressively guiding the network towards more accurate pixel-wise predictions.

Experiments conducted on the SUN RGB-D benchmark, a comprehensive dataset for indoor scene understanding, illustrate the efficacy of RedNet. The RedNet variant employing ResNet-50 showcases a notable mIoU accuracy of 47.8%, establishing competitive benchmarks against existing state-of-the-art methods. These outcomes underscore the potential benefits of integrating residual learning and deep supervision in encoder-decoder models for semantic segmentation tasks, specifically when leveraging complementary depth information.

Notably, the paper demonstrates the advantage of residual connections across both the encoder and decoder. The residual units facilitate the training of deeper networks, which are pivotal for tasks requiring differentiation of subtle class distinctions—common in indoor environments. Moreover, the pyramid supervision strategy appears to significantly enhance model performance, as evidenced by improved accuracy metrics compared to conventional supervision techniques.

The implications of this research extend beyond immediate performance improvements in indoor semantic segmentation. It paves the way for more comprehensive utilization of multimodal data streams (e.g., RGB-D) and emphasizes the importance of architectural choices in dealing with complex scene inputs. Future explorations may delve into refining fusion strategies, optimizing computational efficiency, and extending these principles to other scenarios with diverse environmental conditions.

In conclusion, RedNet exemplifies an adept synthesis of architectural innovations and training strategies to advance the frontier of indoor scene understanding. The integration of depth information through a robust framework underscored by residual learning marks a substantial progression in semantic segmentation methodologies. As researchers continue to push the envelope, further refinements and applications of these concepts are likely to benefit both autonomous systems and broader applications in AI-driven scene interpretation.