多样化大规模数据集！SceneSplat++：首个基于3DGS的综合基准~

Core Insights - The article introduces SceneSplat-Bench, a comprehensive benchmark for evaluating visual-language scene understanding methods based on 3D Gaussian Splatting (3DGS) [11][30]. - It presents SceneSplat-49K, a large-scale dataset containing approximately 49,000 raw scenes and 46,000 filtered 3DGS scenes, which is the most extensive open-source dataset for complex and high-quality scene-level 3DGS reconstruction [9][30]. - The evaluation indicates that generalizable methods consistently outperform per-scene optimization methods, establishing a new paradigm for scalable scene understanding through pre-trained models [30]. Evaluation Protocols - The benchmark evaluates methods based on two key metrics in 3D space: foreground mean Intersection over Union (f-mIoU) and foreground mean accuracy (f-mAcc), addressing object size imbalance and reducing viewpoint dependency compared to 2D evaluations [22][30]. - The evaluation dataset includes ScanNet, ScanNet++, and Matterport3D for indoor scenes, and HoliCity for outdoor scenes, emphasizing the methods' capabilities across various object scales and complex environments [22][30]. Dataset Contributions - SceneSplat-49K is compiled from multiple sources, including SceneSplat-7K, DL3DV-10K, HoliCity, and Aria Synthetic Environments, ensuring a diverse range of indoor and outdoor environments [9][10]. - The dataset preparation involved approximately 891 GPU days and extensive human effort, highlighting the significant resources invested in creating a high-quality dataset [7][9]. Methodological Insights - The article categorizes methods into three types: per-scene optimization methods, per-scene optimization-free methods, and generalizable methods, with SceneSplat representing the latter [23][30]. - Generalizable methods eliminate the need for extensive single-scene computations during inference, allowing for efficient processing of 3D scenes in a single forward pass [24][30]. Performance Results - The results from SceneSplat-Bench demonstrate that SceneSplat excels in both performance and efficiency, often surpassing the pseudo-label methods used for its pre-training [24][30]. - The performance of various methods shows significant variation based on the dataset's complexity, indicating the importance of challenging benchmarks in revealing the limitations of competing methods [28][30].