SceneSplat++: 一个大规模数据集和全面的基准用于语言高斯点云
SceneSplat++: A Large Dataset and Comprehensive Benchmark for Language Gaussian Splatting
AI总结：
SceneSplat++提出一个大规模数据集和基准，评估语言高斯点云方法，展示可推广方法在3D理解中的优势。
摘要：
3D高斯点云（3DGS）作为一种高效的场景几何、外观和语义编码方式，具有高度的性能和效率。此外，将语言与3D场景相结合已被证明是理解3D场景的有效策略。当前的语言高斯点云研究工作主要分为三类：（i）基于场景优化的，（ii）无需场景优化的，以及（iii）可推广的方法。然而，大多数方法仅在少量场景的渲染2D视图上进行评估，并且仅在训练视图附近进行评估，这限制了对整体3D理解能力的洞察。为解决这一差距，我们提出了第一个大规模基准，系统地评估这三类方法，直接在3D空间中进行评估，涵盖三个室内数据集和一个室外数据集的1060个场景。基准测试结果表明，可推广的方法具有明显优势，特别是在放松场景特定限制、在新场景上实现快速前馈推断以及实现优越的分割性能方面。我们进一步引入了GaussianWorld-49K，这是一个精心挑选的3DGS数据集，包含来自多个来源的约49,000个多样化的室内和室外场景，我们展示了可推广的方法能够利用强大的数据先验。我们的代码、基准和数据集已发布在https://scenesplatpp.gaussianworld.ai/。
英文摘要：
3D Gaussian Splatting (3DGS) serves as a highly performant and efficient encoding of scene geometry, appearance, and semantics. Moreover, grounding language in 3D scenes has proven to be an effective strategy for 3D scene understanding. Current Language Gaussian Splatting line of work fall into three main groups: (i) per-scene optimization-based, (ii) per-scene optimization-free, and (iii) generalizable approach. However, most of them are evaluated only on rendered 2D views of a handful of scenes and viewpoints close to the training views, limiting ability and insight into holistic 3D understanding. To address this gap, we propose the first large-scale benchmark that systematically assesses these three groups of methods directly in 3D space, evaluating on 1060 scenes across three indoor datasets and one outdoor dataset. Benchmark results demonstrate a clear advantage of the generalizable paradigm, particularly in relaxing the scene-specific limitation, enabling fast feed-forward inference on novel scenes, and achieving superior segmentation performance. We further introduce GaussianWorld-49K a carefully curated 3DGS dataset comprising around 49K diverse indoor and outdoor scenes obtained from multiple sources, with which we demonstrate the generalizable approach could harness strong data priors. Our codes, benchmark, and datasets are released at https://scenesplatpp.gaussianworld.ai/.
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。
