# Multiscale Feature Fusion with Self-Attention for Efficient 6D Pose Estimation
## 1 Introduction  
The aim of object 6D pose estimation is to estimate the 3D rotation and translation of an object relative to the camera coordinate system, which is a fundamental problem in computer vision. It plays a critical role in a wide range of applications such as robotic manipulation [1, 2, 3, 4], augmented reality [5], and autonomous navigation [6, 7, 8, 9]. However, achieving robust and accurate 6D pose estimation in real-world scenarios remains highly challenging, particularly when objects are textureless, partially occluded, or captured under poor lighting conditions.  
Traditional approaches can be broadly categorized into feature point-based or template-based methods. Feature point-based methods estimate 6D poses by establishing 2D-3 Dcorrespondences between image keypoints and the 3D model [10]. These methods typically exhibit a degraded performance when applied to low-texture objects due to their inability to extract reliable keypoints. Template-based methods, on the other hand, match input images with a pre-rendered template database. While efficient, these methods tend to fail in the presence of occlusions or appearance variations.  
With the rise of deep learning, learning-based approaches have significantly improved the 6D pose estimation performance. Early RGB-based methods such as PoseCNN [11] and BB8 [12] have achieved promising results but are inherently limited by the lack of depth information, making them less robust in complex environments. RGB-D methods, such as DenseFusion [13], leverage complementary depth cues and fuse RGB and point cloud features at the pixel level. Although effective, these approaches rely heavily on PointNet [14] for geometric feature extraction, which is not able to sufficiently capture the local context due to its global feature aggregation design. Later approaches such as PVN3D [15] introduced local feature modeling, but this significantly increased the model complexity and computational cost, limiting their deployment in real-time or resource-constrained applications.  
More recently, large-scale foundation models [16, 17, 18, 19] have demonstrated strong generalization capabilities and robustness through the use of high-capacity architectures and multimodal representations. However, their considerable parameter sizes and inference latency present challenges for real-time deployment and mobile use. Therefore, there is a clear research gap between high-accuracy yet computationally expensive models and lightweight models that have a limited robustness and precision.  
To bridge this gap, we propose a novel lightweight multiscale feature fusion network for 6D pose estimation that achieves an optimal balance between accuracy and efficiency. Specifically, we design a multiscale point cloud feature extraction module using parallel graph convolution layers with varying neighborhood sizes to capture geometric information at different scales. A channel-wise self-attention mechanism is applied to adaptively fuse these features, allowing the model to focus on the most informative scale. In parallel, a lightweight RGB feature extractor using depthwise separable convolutions reduces computations while preserving color cues. Finally, RGB and geometric features are fused at the pixel level to estimate the 6D pose. Compared with existing methods, our approach achieves a competitive accuracy with significantly fewer parameters and faster inference, making it well suited for real-time, mobile deployment.