发表机构
University of Guelph（圭尔夫大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对动态场景重建中未来表面重建缺乏标准基准的问题，引入FutureSurf基准和数据集，通过特定训练与评分方式评估方法，发现现有主干在受控运动及多个场景中有差距，且未来渲染质量与表面精度解耦。
AI 中文摘要
动态场景重建几乎总是在观察到的时间窗口内进行评估，但诸如增强现实覆盖、机器人交互和预期规划等部署设置需要未来表面，即超出捕获时间的几何形状。目前没有标准基准来衡量这一点。我们引入了FutureSurf，这是一个用于未来时间表面重建的受控诊断基准和数据集，它用场景多样性换取精确的未来地面真值和证伪控制。一种方法在序列的前75%上进行训练；我们通过倒角距离在保留的未来上对其提取的每帧表面进行评分，将绝对未来CD作为主要分数，未来/观察差距作为诊断指标。该数据集包含八个解析定义的受控运动，包括三个证伪控制，以及精确的每帧地面真值网格。我们还提供了一个地面真值侧可恢复性预言机。发布内容包括分割文件、评分代码、基准卡和羊角面包元数据。在受控运动上，即使对于原则上可预测的未来（五分之四可通过固定规则从观察到的运动中恢复），DG-Mesh主干也会留下2.7-4.1倍的差距，而证伪控制按设计运行（表面不变运动没有差距）。除了贡献的数据集之外，这种差距在六个动画DG-Mesh资产场景和第二个主干Deformable-3DGS中也存在（2.0-6.6倍；两者共享一个变形-MLP时间模型）。基准还表明，未来渲染质量和未来表面精度在统计上是解耦的，因此该领域报告的新视图合成指标无法跟踪未来几何形状。未来误差是结构化的，集中在表面移动的地方。数据集、评估工具包和评分代码可在Hugging Face和GitHub上获取。
英文摘要
Dynamic-scene reconstruction is almost always evaluated inside the observed time window, yet deployment settings such as AR overlays, robot interaction, and anticipatory planning need the future surface: the geometry at times beyond those captured. No standard benchmark measures this. We introduce FutureSurf, a controlled diagnostic benchmark and dataset for future-time surface reconstruction that trades scene diversity for exact future ground truth and falsification controls. A method trains on the observed first 75% of a sequence; we score its extracted per-frame surface on the held-out future by Chamfer distance, reporting absolute future CD as the primary score and the future/observed gap as a diagnostic. The dataset contains eight analytically defined controlled motions, including three falsification controls, with exact per-frame ground-truth meshes. We also provide a ground-truth-side recoverability oracle. The release includes split files, scoring code, a benchmark card, and Croissant metadata. On the controlled motions, the DG-Mesh backbone leaves a 2.7-4.1$\times$ gap even for futures predictable in principle (four of five recoverable from observed motion by a fixed rule), while the falsification controls behave as designed (the surface-invariant motion shows no gap). Beyond the contributed dataset, the gap persists across six animated DG-Mesh asset scenes and a second backbone, Deformable-3DGS (2.0-6.6$\times$; both share a deformation-MLP temporal model). The benchmark also shows that future rendering quality and future-surface accuracy are statistically decoupled, so the novel-view-synthesis metrics the field reports do not track future geometry. The future error is structured, concentrating where the surface moves. The dataset, evaluation toolkit, and scoring code are available on Hugging Face and GitHub (https://github.com/Ricky-S/futuresurf).
CommentsSee https://github.com/Ricky-S/futuresurf
