RedNet: Residual Encoder-Decoder Network for indoor RGB-D Semantic Segmentation
RedNet employs residual encoder-decoder with depth fusion and pyramid supervision, achieving 47.8% mIoU on SUN RGB-D.
Key Findings
Methodology
RedNet builds upon ResNet-50, utilizing residual blocks as core units in both encoder and decoder. The network processes RGB and depth separately through parallel branches, with multi-layer fusion to integrate depth cues. Pyramid supervision applies multi-scale loss at various decoder layers, addressing gradient vanishing. Training employs weighted cross-entropy with data augmentation. Results show 47.8% mIoU, outperforming previous models, demonstrating effective multi-modal feature learning and deep supervision.
Key Results
- RedNet(ResNet-50) achieves 47.8% mIoU on SUN RGB-D, surpassing prior methods like RefineNet and FuseNet. Multi-layer supervision improves convergence and accuracy by over 3%. Ablation studies confirm that residual modules, depth fusion, and pyramid supervision are critical. The model maintains computational efficiency while delivering high-precision scene understanding, especially in cluttered indoor environments.
- Compared to ResNet-34, ResNet-50 version improves accuracy by about 2%. Incorporating pyramid supervision yields a 3% boost over baseline. The model demonstrates robustness across categories such as furniture, walls, and floors, with consistent performance gains. It outperforms state-of-the-art models in accuracy and efficiency, making it suitable for real-time indoor scene applications.
- The model's stability across various indoor scenarios indicates strong generalization. Its ability to effectively fuse RGB and depth features addresses the challenge of ambiguous visual cues. Results suggest that deep residual learning combined with multi-scale supervision is a promising direction for complex scene segmentation, with potential extensions to multi-task learning and real-world deployment.
Significance
This work advances indoor scene understanding by integrating residual learning, depth fusion, and multi-level supervision. The architecture overcomes training difficulties of deep networks, enhances multi-modal feature extraction, and sets new benchmarks on SUN RGB-D. Its success paves the way for more accurate robotic perception, smart home automation, and AR/VR applications. The combination of residual blocks and pyramid supervision provides a scalable framework for future multi-sensor scene analysis, addressing longstanding challenges in spatial perception and scene comprehension.
Technical Contribution
RedNet innovatively combines residual encoder-decoder architecture with depth fusion and pyramid supervision, offering a unified framework that improves training stability and feature integration. Residual modules mitigate vanishing gradients, while multi-layer fusion enhances multi-modal feature learning. The pyramid supervision strategy ensures effective gradient flow across multiple decoder stages. These contributions collectively push the boundary of indoor RGB-D semantic segmentation, enabling deeper, more accurate models with practical efficiency.
Novelty
This is the first work to integrate residual encoder-decoder architecture with multi-layer depth fusion and pyramid supervision specifically for indoor RGB-D scene segmentation. Unlike prior models that treat fusion or supervision separately, RedNet combines both in a cohesive design, leading to superior performance. Its multi-scale supervision and residual residual learning approach address core issues like gradient vanishing and feature degradation, representing a significant step forward in multi-modal deep learning.
Limitations
- Despite high accuracy, the model's computational complexity remains substantial, limiting deployment on resource-constrained devices. The reliance on high-quality depth maps makes it sensitive to sensor noise and missing data. In extremely cluttered or poorly lit environments, performance may degrade due to depth inaccuracies. Future work should focus on model compression, noise robustness, and real-time inference optimization.
AI Executive Summary
Indoor scene understanding is crucial for applications like robotics, augmented reality, and smart homes. Traditional methods struggled with complex environments, occlusions, and multi-modal data integration. Recent advances introduced deep neural networks, but training very deep models faced issues like gradient vanishing and information loss. To address these challenges, this study proposes RedNet, a residual encoder-decoder architecture that incorporates depth fusion and multi-scale supervision.
RedNet leverages residual blocks in both encoder and decoder, ensuring stable training of deep networks. The depth information is processed through dedicated branches and fused at multiple layers, enriching scene features. The innovative pyramid supervision applies loss functions at various decoder stages, promoting effective gradient flow and better convergence. Extensive experiments on SUN RGB-D demonstrate that RedNet achieves 47.8% mIoU, outperforming existing methods.
The architecture's ability to fuse multi-modal data and maintain spatial details significantly enhances indoor scene segmentation accuracy. This progress opens new avenues for autonomous robots and intelligent systems to perceive environments more reliably. While computational costs are still high, ongoing work aims to optimize efficiency. Overall, RedNet marks a substantial step forward in indoor RGB-D semantic segmentation, with promising implications for both research and industry.
Deep Analysis
Background
随着深度学习的兴起，语义分割已成为场景理解的核心任务。早期方法多依赖手工特征，效果有限。近年来，端到端的全卷积网络（FCN）和U-Net等架构极大推动了性能提升。特别是在室内场景中，RGB-D数据的引入显著改善了识别效果。代表性工作如SegNet、RefineNet和FuseNet结合残差和多尺度特征，取得了较好表现。然而，深层网络训练中的梯度消失和信息丢失仍是难题。深度融合技术逐渐成为研究热点，但多模态信息的高效利用仍需突破。
Core Problem
室内RGB-D场景语义分割面临多重挑战，包括复杂光照、遮挡、多模态信息融合不足以及深层网络训练困难。传统方法难以兼顾空间细节和语义理解，导致识别精度不足。深度噪声和多尺度特征融合的有效策略亟待开发。此外，梯度消失和模型退化限制了深层网络的潜力，亟需创新架构和训练策略解决这些瓶颈。
Innovation
本研究提出RedNet，结合残差学习、多层次深度融合和金字塔监督，创新点在于：1）在编码器和解码器中均采用残差模块，缓解深层退化；2）引入多层深度融合结构，增强RGB与深度信息的结合；3）在多尺度解码层引入监督，改善梯度传递。这些设计显著提升了复杂室内场景中的识别能力，突破了现有技术的局限。
Methodology
- �� 输入：RGB图像与深度图
- �� 编码器：采用ResNet-50架构，分别处理RGB和深度路径，残差块逐层提取特征
- �� 融合：在多层中将深度特征与RGB特征逐层融合，采用逐元素相加
- �� 解码器：利用上采样残差单元逐步还原空间分辨率，跳跃连接保持空间细节
- �� 监督：在多个解码层引入侧输出，采用交叉熵损失，结合全局输出优化
- �� 训练：多尺度损失加权，采用SGD或Adam优化，数据增强提升泛化能力
Experiments
在SUN RGB-D数据集上进行训练与评估，使用预训练ResNet-50作为编码器。采用多尺度损失和数据增强策略，比较不同模型（ResNet-34、ResNet-50）及无金字塔监督的效果。指标包括mIoU、像素准确率等，进行消融实验验证各模块贡献。训练细节包括学习率调度、批次大小和正则化参数，确保模型收敛稳定。
Results
RedNet(ResNet-50)在SUN RGB-D上实现了47.8%的mIoU，优于RefineNet和FuseNet等模型。引入金字塔监督后，性能提升超过3%，验证其有效性。ResNet-50版本在保持较低参数的同时，显著优于ResNet-34，展现深层网络优势。深度融合和残差结构的结合，增强了复杂场景中的识别能力，尤其在遮挡和多光照条件下表现优异。
Applications
该模型适用于室内机器人导航、智能家居场景理解、虚拟现实环境构建等。只需配备RGB-D传感器，即可实现高精度场景语义理解，为自动化和交互提供技术支撑。未来可结合多任务学习，拓展目标检测和实例分割，推动多模态空间感知的发展。
Limitations & Outlook
模型对深度噪声敏感，复杂遮挡和极端光照条件下仍存在误差。训练成本较高，硬件资源需求大，限制了在边缘设备上的部署。模型结构较复杂，推理速度需优化，未来需研究轻量化方案。同时，深度融合策略在极端场景下仍有提升空间。
Plain Language Accessible to non-experts
想象你在厨房做饭，食材代表不同的场景信息。普通厨师只用看食材的颜色和形状（RGB图像），但有经验的厨师还会用味道和温度（深度信息）来判断。RedNet就像这个经验厨师，它不仅看表面，还结合“味道”信息，能更准确地识别厨房里每个物品。它用一种特别的“烹饪方法”——残差结构，让深层次的“厨艺”不出错，还用“多层调味”——金字塔监督，让每一步都调得恰到好处。这样，厨师（模型）就能在复杂的厨房环境中，快速、准确地识别每样食材（场景中的物体），帮助机器人更好地工作。这就像你用心记住每个菜的味道一样，RedNet也记住了场景的细节，变得越来越聪明。
ELI14 Explained like you're 14
想象你在学校的科学实验室里，老师让你用不同的工具去识别各种物品。有时候只用看颜色和形状（像普通的图片），但有时候还需要用特殊的仪器（像深度信息）来更清楚地知道物品的大小和距离。RedNet就像一个聪明的学生，既会用眼睛观察，也会用仪器测量，能更准确地知道房间里每样东西是什么。它用一种特别的学习方法，让每一步都变得更稳，避免出错。这样，无论房间多复杂，它都能帮机器人认清里面的物品，就像你在玩拼图游戏，越玩越厉害。未来，这个学生还会学会更多技能，帮机器人在家里、学校里做得更好！
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.