Q-SLAM：面向单目SLAM的二次曲面表示
Q-SLAM: Quadric Representations for Monocular SLAM
AI总结：
本文提出Q-SLAM，利用二次曲面分解刚性场景以重塑体积表示，通过二次曲面假设校正RGB深度估计并设计二次曲面分解Transformer聚合信息，在单目SLAM中仅依赖估计深度即可达到接近真值深度方法的精度。
摘要：
在本文中，我们通过二次曲面的视角重新构想体积表示。我们假设刚性场景组件可以被有效地分解为二次曲面。利用这一假设，我们用若干二次曲面平面重塑了由数百万个立方体构成的体积表示，从而在SLAM场景中实现了更准确、更高效的三维场景建模。首先，我们利用二次曲面假设来校正来自RGB输入的噪声深度估计。这一步骤显著提高了深度估计精度，并使我们能够高效地在二次曲面平面周围采样光线点，而不是像以往的NeRF-SLAM系统那样在整个体积空间中进行采样。其次，我们引入了一种新颖的二次曲面分解Transformer，用于跨二次曲面聚合信息。二次曲面语义不仅被显式地用于深度校正和场景分解，而且还作为映射网络的隐式监督信号。通过严格的实验评估，我们的方法在依赖估计深度的其他方法中表现出优越的性能，并在合成数据集和真实世界数据集上达到了与利用真值深度的方法相当的精度。
英文摘要：
In this paper, we reimagine volumetric representations through the lens of quadrics. We posit that rigid scene components can be effectively decomposed into quadric surfaces. Leveraging this assumption, we reshape the volumetric representations with million of cubes by several quadric planes, which results in more accurate and efficient modeling of 3D scenes in SLAM contexts. First, we use the quadric assumption to rectify noisy depth estimations from RGB inputs. This step significantly improves depth estimation accuracy, and allows us to efficiently sample ray points around quadric planes instead of the entire volume space in previous NeRF-SLAM systems. Second, we introduce a novel quadric-decomposed transformer to aggregate information across quadrics. The quadric semantics are not only explicitly used for depth correction and scene decomposition, but also serve as an implicit supervision signal for the mapping network. Through rigorous experimental evaluation, our method exhibits superior performance over other approaches relying on estimated depth, and achieves comparable accuracy to methods utilizing ground truth depth on both synthetic and real-world datasets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
