  3D question answering is a young field in 3D vision-language that is yet to
be explored. Previous methods are limited to a pre-defined answer space and
cannot generate answers naturally. In this work, we pivot the question
answering task to a sequence generation task to generate free-form natural
answers for questions in 3D scenes (Gen3DQA). To this end, we optimize our
model directly on the language rewards to secure the global sentence semantics.
Here, we also adapt a pragmatic language understanding reward to further
improve the sentence quality. Our method sets a new SOTA on the ScanQA
benchmark (CIDEr score 72.22/66.57 on the test sets).
