构建首个面向航拍场景的综合重建数据集，量化复杂度提升模型评估可靠性
ClaraVid: A Holistic Scene Reconstruction Benchmark From Aerial Perspective With Delentropy-Based Complexity Profiling
- 设计合成航拍数据集ClaraVid，含16,917张高分辨率图像与多模态标注
- 提出基于微分熵的景深复杂度指标（DSP），发现复杂度越高重建误差越大
- 开源数据与代码，适用于3D重建、语义分割等任务的研究者
航拍全景场景理解算法的发展受限于缺乏能同时支持语义与几何重建的全面数据集。现有合成数据集存在任务专一、场景不真实、渲染伪影等问题。本文提出ClaraVid，一个专为克服上述缺陷而设计的合成航拍数据集，包含16,917张4032x3024分辨率图像，覆盖多种地形，提供密集深度图、全景分割、稀疏点云及动态物体掩码，并有效减少常见渲染伪影。为进一步推进神经重建研究，提出德尔熵场景轮廓（Delentropic Scene Profile, DSP），通过微分熵分析构建全新复杂度度量，可定量评估场景难度。利用DSP系统性地评估神经重建方法，发现场景复杂度与重建精度间存在稳定显著相关性：德尔熵越高，重建误差越大，验证了DSP作为可靠复杂度先验的有效性。数据与代码已公开于https://rdbch.github.io/claravid/
原文摘要 · Abstract (English)
The development of aerial holistic scene understanding algorithms is hindered by the scarcity of comprehensive datasets that enable both semantic and geometric reconstruction. While synthetic datasets offer an alternative, existing options exhibit task-specific limitations, unrealistic scene compositions, and rendering artifacts that compromise real-world applicability. We introduce ClaraVid, a synthetic aerial dataset specifically designed to overcome these limitations. Comprising 16,917 high-resolution images captured at 4032x3024 from multiple viewpoints across diverse landscapes, ClaraVid provides dense depth maps, panoptic segmentation, sparse point clouds, and dynamic object masks, while mitigating common rendering artifacts. To further advance neural reconstruction, we introduce the Delentropic Scene Profile (DSP), a novel complexity metric derived from differential entropy analysis, designed to quantitatively assess scene difficulty and inform reconstruction tasks. Utilizing DSP, we systematically benchmark neural reconstruction methods, uncovering a consistent, measurable correlation between scene complexity and reconstruction accuracy. Empirical results indicate that higher delentropy strongly correlates with increased reconstruction errors, validating DSP as a reliable complexity prior. The data and code are available on the project page at https://rdbch.github.io/claravid/
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