论文标题
点云的显着对象检测
Salient Object Detection for Point Clouds
论文作者
论文摘要
本文研究了未开发的任务点云突出对象检测（SOD）。与图像的SOD不同，我们发现点云的注意力转移可能会引起显着冲突，即矛盾的对象属于显着性和非偏好类别。为了避免此问题，我们提出了一个针对明显对象的新颖的观点观点，可以合理地反映出点云场景中最引人注目的对象。按照此公式，我们介绍了PCSOD，这是针对点云SOD的第一个数据集，该数据集由2,872个内部/外门3D视图组成。我们的数据集中的样本标记为层次注释，例如超级/子类，边界框和分割图，该映射赋予了我们数据集的出色概括性和广泛的适用性，以验证各种猜想。为了证明我们解决方案的可行性，我们进一步贡献了基线模型和基准五个代表性模型，以进行全面比较。提出的模型可以有效地分析检测显着物体的不规则和无序点。由于结合了任务范围的设计，我们的方法表现出与其他基线相比的可见优势，从而产生了更令人满意的结果。广泛的实验和讨论揭示了该研究领域的有希望的潜力，为进一步的研究铺平了道路。
This paper researches the unexplored task-point cloud salient object detection (SOD). Differing from SOD for images, we find the attention shift of point clouds may provoke saliency conflict, i.e., an object paradoxically belongs to salient and non-salient categories. To eschew this issue, we present a novel view-dependent perspective of salient objects, reasonably reflecting the most eye-catching objects in point cloud scenarios. Following this formulation, we introduce PCSOD, the first dataset proposed for point cloud SOD consisting of 2,872 in-/out-door 3D views. The samples in our dataset are labeled with hierarchical annotations, e.g., super-/sub-class, bounding box, and segmentation map, which endows the brilliant generalizability and broad applicability of our dataset verifying various conjectures. To evidence the feasibility of our solution, we further contribute a baseline model and benchmark five representative models for a comprehensive comparison. The proposed model can effectively analyze irregular and unordered points for detecting salient objects. Thanks to incorporating the task-tailored designs, our method shows visible superiority over other baselines, producing more satisfactory results. Extensive experiments and discussions reveal the promising potential of this research field, paving the way for further study.
