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3D question answering is a young field in 3D vision-language that is yet tobe explored. Previous methods are limited to a pre-defined answer space andcannot generate answers naturally. In this work, we pivot the questionanswering task to a sequence generation task to generate free-form naturalanswers for questions in 3D scenes (Gen3DQA). To this end, we optimize ourmodel directly on the language rewards to secure the global sentence semantics.Here, we also adapt a pragmatic language understanding reward to furtherimprove the sentence quality. Our method sets a new SOTA on the ScanQAbenchmark (CIDEr score 72.22/66.57 on the test sets).

Generating Context-Aware Natural Answers for Questions in 3D Scenes