  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.
