Mulindwa Desire Burume, Du Shengzhi, Liu Qingxue
Department of Electrical Engineering, Tshwane University of Technology, Pretoria 0001, South Africa.
School of Mechanical and Electrical Engineering, Kunming University, Kunming 650214, China.
Sensors (Basel). 2024 Nov 12;24(22):7215. doi: 10.3390/s24227215.
The progress of 3D instance segmentation techniques has made it essential for several applications, such as augmented reality, autonomous driving, and robotics. Traditional methods usually have challenges with complex indoor scenes made of multiple objects with different occlusions and orientations. In this work, the authors present an innovative model that integrates a new adaptive n-shifted shuffle (ANSS) attention mechanism with the Generalized Hough Transform (GHT) for robust 3D instance segmentation of indoor scenes. The proposed technique leverages the n-shifted sigmoid activation function, which improves the adaptive shuffle attention mechanism, permitting the network to dynamically focus on relevant features across various regions. A learnable shuffling pattern is produced through the proposed ANSS attention mechanism to spatially rearrange the relevant features, thus augmenting the model's ability to capture the object boundaries and their fine-grained details. The integration of GHT furnishes a vigorous framework to localize and detect objects in the 3D space, even when heavy noise and partial occlusions are present. The authors evaluate the proposed method on the challenging Stanford 3D Indoor Spaces Dataset (S3DIS), where it establishes its superiority over existing methods. The proposed approach achieves state-of-the-art performance in both mean Intersection over Union (IoU) and overall accuracy, showcasing its potential for practical deployment in real-world scenarios. These results illustrate that the integration of the ANSS and the GHT yields a robust solution for 3D instance segmentation tasks.
3D实例分割技术的进步使其在增强现实、自动驾驶和机器人技术等多个应用领域变得至关重要。传统方法在处理由多个具有不同遮挡和方向的物体组成的复杂室内场景时通常面临挑战。在这项工作中，作者提出了一种创新模型，该模型将一种新的自适应n移位混洗（ANSS）注意力机制与广义霍夫变换（GHT）相结合，用于室内场景的稳健3D实例分割。所提出的技术利用n移位Sigmoid激活函数，改进了自适应混洗注意力机制，使网络能够动态地关注各个区域的相关特征。通过所提出的ANSS注意力机制产生一个可学习的混洗模式，以便在空间上重新排列相关特征，从而增强模型捕捉物体边界及其细粒度细节的能力。即使存在大量噪声和部分遮挡，GHT的集成也为在3D空间中定位和检测物体提供了一个强大的框架。作者在具有挑战性的斯坦福3D室内空间数据集（S3DIS）上评估了所提出的方法，该方法在该数据集上显示出优于现有方法的优势。所提出的方法在平均交并比（IoU）和总体准确率方面均达到了当前的最优性能，展示了其在实际场景中实际部署的潜力。这些结果表明，ANSS和GHT的集成产生了一种用于3D实例分割任务的稳健解决方案。
