Recently, 3D scenes parsing with deep learning approaches has been a heating topic. However, current methods with fully-supervised models require manually annotated point-wise supervision which is extremely user-unfriendly and time-consuming to obtain. As such, training 3D scene parsing models with sparse supervision is an intriguing alternative. We term this task as data-efficient 3D scene parsing and propose an effective two-stage framework named VIBUS to resolve it by exploiting the enormous unlabeled points. In the first stage, we perform self-supervised representation learning on unlabeled points with the proposed Viewpoint Bottleneck loss function. The loss function is derived from an information bottleneck objective imposed on scenes under different viewpoints, making the process of representation learning free of degradation and sampling. In the second stage, pseudo labels are harvested from the sparse labels based on uncertainty-spectrum modeling. By combining data-driven uncertainty measures and 3D mesh spectrum measures (derived from normal directions and geodesic distances), a robust local affinity metric is obtained. Finite gamma/beta mixture models are used to decompose category-wise distributions of these measures, leading to automatic selection of thresholds. We evaluate VIBUS on the public benchmark ScanNet and achieve state-of-the-art results on both validation set and online test server. Ablation studies show that both Viewpoint Bottleneck and uncertainty-spectrum modeling bring significant improvements. Codes and models are publicly available at https://github.com/AIR-DISCOVER/VIBUS.

本研究提出了一种名为VIBUS的有效的两阶段框架，用于通过利用巨大的未标记点解决数据有效的3D场景解析任务。通过在未标记点上进行自我监督表示学习和基于不确定性光谱建模从稀疏标签中收集伪标签，该方法在公共基准ScanNet上实现了最先进的结果，克服了当前使用完全监督模型的局限性。

VIBUS：采用视角瓶颈和不确定性谱建模的高效3D场景解析