High-confidence overlap prediction and accurate correspondences are critical for cutting-edge models to align paired point clouds in a partial-to-partial manner. However, there inherently exists uncertainty between the overlapping and non-overlapping regions, which has always been neglected and significantly affects the registration performance. Beyond the current wisdom, we propose a novel uncertainty-aware overlap prediction network, dubbed UTOPIC, to tackle the ambiguous overlap prediction problem; to our knowledge, this is the first to explicitly introduce overlap uncertainty to point cloud registration. Moreover, we induce the feature extractor to implicitly perceive the shape knowledge through a completion decoder, and present a geometric relation embedding for Transformer to obtain transformation-invariant geometry-aware feature representations. With the merits of more reliable overlap scores and more precise dense correspondences, UTOPIC can achieve stable and accurate registration results, even for the inputs with limited overlapping areas. Extensive quantitative and qualitative experiments on synthetic and real benchmarks demonstrate the superiority of our approach over state-of-the-art methods. Code is available at https://github.com/ZhileiChen99/UTOPIC.
翻译:高度自信重叠的预测和准确的通信对于尖端模型以部分到部分的方式对对齐点云云进行对齐至关重要。然而,重叠和非重叠区域之间本来就存在着不确定性,这些区域一直被忽视,对登记工作产生了重大影响。除了目前的智慧外,我们提议建立一个新颖的不确定性意识重叠预测网络,称为UTOPIC,以解决模糊的重叠预测问题;据我们所知,这是第一个将重叠的不确定性明确引入点云登记册的前沿模型。此外,我们诱导特征提取器通过完成解码暗含地感知形状知识,并为变异器提供几何联系,以获得变异性几何地貌特征说明。由于比较可靠的重叠得分数和更精确的通信,UTOPIC可以取得稳定和准确的登记结果,即使是在有限的重叠领域的投入方面也是如此。关于合成基准和真实基准的广泛定量和定性实验表明我们的方法优于最新方法。代码见https://github.com/Zhilei99/UTOPIC。