Bridging the Gap between 2D and 3D Visual Question Answering: A Fusion Approach for 3D VQA

In 3D Visual Question Answering (3D VQA), the scarcity of fully annotateddata and limited visual content diversity hampers the generalization to novelscenes and 3D concepts (e.g., only around 800 scenes are utilized in ScanQA andSQA dataset). Current approaches resort supplement 3D reasoning with 2Dinformation. However, these methods face challenges: either they use top-down2D views that introduce overly complex and sometimes question-irrelevant visualclues, or they rely on globally aggregated scene/image-level representationsfrom 2D VLMs, losing the fine-grained vision-language correlations. To overcomethese limitations, our approach utilizes question-conditional 2D view selectionprocedure, pinpointing semantically relevant 2D inputs for crucial visualclues. We then integrate this 2D knowledge into the 3D-VQA system via atwo-branch Transformer structure. This structure, featuring a Twin-Transformerdesign, compactly combines 2D and 3D modalities and captures fine-grainedcorrelations between modalities, allowing them mutually augmenting each other.Integrating proposed mechanisms above, we present BridgeQA, that offers a freshperspective on multi-modal transformer-based architectures for 3D-VQA.Experiments validate that BridgeQA achieves state-of-the-art on 3D-VQA datasetsand significantly outperforms existing solutions. Code is available at$\href{https://github.com/matthewdm0816/BridgeQA}{\text{this URL}}$.