Detecting objects and estimating their 6D poses from a single RGB image is quite challenging under severe occlusions. Recently, vector-field based methods have shown certain robustness to occlusion and truncation. Based on the vector-field representation, applying voting strategy to localize 2D keypoints can further reduce the influence of outliers. To improve the effectiveness of vector-field based deep network and voting scheme, we propose Atrous Spatial Pyramid Pooling and Distance-Filtered PVNet (ASPP-DF-PVNet), an occlusion resistant framework for 6D object pose estimation. ASPP-DF-PVNet utilizes the effective Atrous Spatial Pyramid Pooling (ASPP) module of Deeplabv3 to capture multi-scale features and encode global context information, which improves the accuracy of segmentation and vector-field prediction comparing to the original PVNet, especially under severe occlusions. Considering that the distances between pixels and keypoint hypotheses will affect the voting deviations, we then present a distance-filtered voting scheme which takes the voting distances into consideration to filter out the votes with large deviations. Experiments demonstrate that our method outperforms the state-of-the-art methods by a considerable margin without using pose refinement, and obtains competitive results against the methods with refinement on the LINEMOD and Occlusion LINEMOD datasets.

在严重遮挡下，从单个RGB图像检测对象并估计其6D姿势是非常具有挑战性的。近来，基于矢量场的方法已经显示出对遮挡和截断的一定鲁棒性。基于矢量场表示，应用投票策略定位2D关键点可以进一步减少异常值的影响。为了提高基于矢量场的深度网络和投票方案的有效性，我们提出了Atrous空间金字塔池和距离过滤PVNet（ASPP-DF-PVNet），这是一种用于6D对象姿态估计的抗阻塞框架。ASPP-DF-PVNet利用Deeplabv3的有效Atrous空间金字塔池（ASPP）模块捕获多尺度特征并编码全局上下文信息，与原始PVNet相比，该算法提高了分割和矢量场预测的准确性，特别是在严重咬合的情况下。考虑到像素和关键点假设之间的距离会影响投票偏差，因此我们提出了一种距离过滤的投票方案，该方案将投票距离考虑在内，以过滤出偏差较大的投票。实验表明，我们的方法在不使用姿势优化的情况下，在一定程度上优于最新方法，并且在LINEMOD和Occlusion LINEMOD数据集上获得了与改进方法相比有竞争力的结果。