Chamfer Distance (CD) and Earth Mover's Distance (EMD) are two broadly
adopted metrics for measuring the similarity between two point sets. However,
CD is usually insensitive to mismatched local density, and EMD is usually
dominated by global distribution while overlooks the fidelity of detailed
structures. Besides, their unbounded value range induces a heavy influence from
the outliers. These defects prevent them from providing a consistent
evaluation. To tackle these problems, we propose a new similarity measure named
Density-aware Chamfer Distance (DCD). It is derived from CD and benefits from
several desirable properties: 1) it can detect disparity of density
distributions and is thus a more intensive measure of similarity compared to
CD; 2) it is stricter with detailed structures and significantly more
computationally efficient than EMD; 3) the bounded value range encourages a
more stable and reasonable evaluation over the whole test set. We adopt DCD to
evaluate the point cloud completion task, where experimental results show that
DCD pays attention to both the overall structure and local geometric details
and provides a more reliable evaluation even when CD and EMD contradict each
other. We can also use DCD as the training loss, which outperforms the same
model trained with CD loss on all three metrics. In addition, we propose a
novel point discriminator module that estimates the priority for another guided
down-sampling step, and it achieves noticeable improvements under DCD together
with competitive results for both CD and EMD. We hope our work could pave the
way for a more comprehensive and practical point cloud similarity evaluation.
Our code will be available at:
this https URL .

提出一种新的点云相似度评价方法 ——Density-aware Chamfer Distance (DCD)，结合 point cloud completion 任务和 point discriminator 模块的实验结果表明，DCD 关注整体结构和局部几何细节并提供更可靠的评价。