DF3DV-1K：一种用于无干扰新视角生成的大规模数据集与基准测试集
DF3DV-1K: A Large-Scale Dataset and Benchmark for Distractor-Free Novel View Synthesis
摘要
光场技术的进步使得真实感强的新视角合成成为可能。在多个领域，人们已经建立了大规模的真实世界数据集，这些数据集有助于进行全面的评估，从而推动技术发展，超越特定场景的重建方法。不过，对于没有干扰元素的光场技术来说，仍然缺乏包含清晰与杂乱图像的大规模数据集，这限制了相关技术的发展。为了填补这一空白，我们推出了DF3DV-1K这个大规模真实世界数据集，该数据集包含1,048个场景，每个场景都包含清晰与杂乱的图像集，可用于评估。该数据集共包含89,924张由消费级相机拍摄的图像，涵盖了128种干扰元素类型和161种室内外环境主题。其中，41个场景的子集DF3DV-41被专门设计用于评估无干扰光场技术在复杂场景下的可靠性。利用DF3DV-1K，我们对九种最新的无干扰光场技术和3D高斯斑点技术进行了评估，确定了最可靠的技术以及最具挑战性的场景。除了评估之外，我们还展示了DF3DV-1K在改进基于扩散技术的2D增强技术方面的应用效果，在保留集和On-the-go数据集上，平均提升了0.96 dB PSNR和0.057 LPIPS。我们希望DF3DV-1K能够促进无干扰视觉技术的发展，推动技术向更先进的方向发展。该数据集和评估表可访问于https://johnnylu305.github.io/df3dv1k_web/。
English Abstract
Advances in radiance fields have enabled photorealistic novel view synthesis. In several domains, large-scale real-world datasets have been developed to support comprehensive benchmarking and to facilitate progress beyond scene-specific reconstruction. However, for distractor-free radiance fields, a large-scale dataset with clean and cluttered images per scene remains lacking, limiting the development. To address this gap, we introduce DF3DV-1K, a large-scale real-world dataset comprising 1,048 scenes, each providing clean and cluttered image sets for benchmarking. In total, the dataset contains 89,924 images captured using consumer cameras to mimic casual capture, spanning 128 distractor types and 161 scene themes across indoor and outdoor environments. A curated subset of 41 scenes, DF3DV-41, is systematically designed to evaluate the robustness of distractor-free radiance field methods under challenging scenarios. Using DF3DV-1K, we benchmark nine recent distractor-free radiance field methods and 3D Gaussian Splatting, identifying the most robust methods and the most challenging scenarios. Beyond benchmarking, we demonstrate an application of DF3DV-1K by fine-tuning a diffusion-based 2D enhancer to improve radiance field methods, achieving average improvements of 0.96 dB PSNR and 0.057 LPIPS on the held-out set (e.g., DF3DV-41) and the On-the-go dataset. We hope DF3DV-1K facilitates the development of distractor-free vision and promotes progress beyond scene-specific approaches. The dataset and leaderboard are available at https://johnnylu305.github.io/df3dv1k_web/.