CausalSplat: 3Dガウシアンスプラッティングにおける包括的階層的推論に向けて
CausalSplat: Towards Comprehensive Hierarchical Reasoning in 3D Gaussian Splatting
3Dガウシアンスプラッティングを用いたシーン理解において、暗黙的な意図や常識推論を扱う新しいタスクを提案し、ベンチマークとフレームワークCausalSplatを構築した。
著者: Jiayu Ding, Meilu Song, Yun Chen, Wei Gao, Ge Li
分類: cs.CV
原文アブストラクト
While 3D Gaussian Splatting (3DGS) has advanced open vocabulary scene understanding, existing methods remain confined to explicit queries. They struggle to interpret implicit intents, complex spatial constraints, and commonsense reasoning required for practical embodied interactions. To address this gap, we introduce the task of reasoning 3D Gaussian segmentation and construct two benchmarks, Causal-LERF and Causal-ScanNet. These benchmarks systematically evaluate commonsense, spatial, affordance, and counterfactual reasoning. Evaluations reveal that current state of the art methods perform poorly on these reasoning challenges. Therefore, we propose CausalSplat, a framework that integrates vision-language models with 3D scene graphs to disentangle explicit structural perception from implicit logical inference. Extensive experiments demonstrate that CausalSplat achieves state of the art performance on our reasoning benchmarks while showing strong generalizability on standard referring and open vocabulary 3D segmentation tasks. Project Page: https://jiayuding031020.github.io/CausalSplat