SceneBench: A Hierarchical Benchmark for Vision-Language Understanding of 3D Scenes
Vision-language models excel at 2D image understanding but remain limited in 3D spatial reasoning. Progress is hindered by limitations in current benchmarks. First, 3D datasets often rely on point clouds that capture geometry but discard rich visual features like texture, text, and materials. Second, annotations treat objects in isolation while ignoring real-world hierarchical organization (scenes, rooms, functional areas, object groups). Third, evaluation tasks focus narrowly on basic recognition rather than multi-step spatial reasoning. In this context, we introduce SceneBench, a benchmark of 966 photorealistic 3D scenes reconstructed with Gaussian Splatting and densely annotated with h...
| Authors | Anubhav Khanal, Prabigya Acharya, Roshni Poudel et al. |
|---|---|
| Primary category | cs.CV |
| Published | 14 September 2026 |
| Last updated at source | 14 September 2026 |
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