SIG3D is a novel approach for 3D vision language reasoning that emphasizes situational awareness. The paper highlights the importance of situational awareness in 3D vision language tasks, where an autonomous agent must understand its position and orientation in a 3D environment to answer questions. The authors introduce SIG3D, an end-to-end model that tokenizes 3D scenes into sparse voxel representations and uses a language-grounded situation estimator followed by a situated question answering module. The model outperforms state-of-the-art methods in both situation estimation and question answering, with a significant improvement in situation estimation accuracy. The model's architecture includes an anchor-based situation estimation strategy and a situation-guided visual re-encoding strategy, which enhance the model's ability to perceive the environment from the agent's intended perspective. The model is evaluated on two challenging 3D visual question answering datasets, SQA3D and ScanQA, and demonstrates superior performance in both situation estimation and question answering tasks. The paper also includes a pilot study that highlights the importance of situational awareness in 3D reasoning tasks and shows that existing methods lack effective situation estimation. The authors conclude that their model significantly improves 3D vision language reasoning by incorporating situational awareness into the model's architecture.SIG3D is a novel approach for 3D vision language reasoning that emphasizes situational awareness. The paper highlights the importance of situational awareness in 3D vision language tasks, where an autonomous agent must understand its position and orientation in a 3D environment to answer questions. The authors introduce SIG3D, an end-to-end model that tokenizes 3D scenes into sparse voxel representations and uses a language-grounded situation estimator followed by a situated question answering module. The model outperforms state-of-the-art methods in both situation estimation and question answering, with a significant improvement in situation estimation accuracy. The model's architecture includes an anchor-based situation estimation strategy and a situation-guided visual re-encoding strategy, which enhance the model's ability to perceive the environment from the agent's intended perspective. The model is evaluated on two challenging 3D visual question answering datasets, SQA3D and ScanQA, and demonstrates superior performance in both situation estimation and question answering tasks. The paper also includes a pilot study that highlights the importance of situational awareness in 3D reasoning tasks and shows that existing methods lack effective situation estimation. The authors conclude that their model significantly improves 3D vision language reasoning by incorporating situational awareness into the model's architecture.