  Text-based Visual Question Answering~(TextVQA) aims to produce correct
answers for given questions about the images with multiple scene texts. In most
cases, the texts naturally attach to the surface of the objects. Therefore,
spatial reasoning between texts and objects is crucial in TextVQA. However,
existing approaches are constrained within 2D spatial information learned from
the input images and rely on transformer-based architectures to reason
implicitly during the fusion process. Under this setting, these 2D spatial
reasoning approaches cannot distinguish the fine-grain spatial relations
between visual objects and scene texts on the same image plane, thereby
impairing the interpretability and performance of TextVQA models. In this
paper, we introduce 3D geometric information into a human-like spatial
reasoning process to capture the contextual knowledge of key objects
step-by-step. %we formulate a human-like spatial reasoning process by
introducing 3D geometric information for capturing key objects' contextual
knowledge. To enhance the model's understanding of 3D spatial relationships,
Specifically, (i)~we propose a relation prediction module for accurately
locating the region of interest of critical objects; (ii)~we design a
depth-aware attention calibration module for calibrating the OCR tokens'
attention according to critical objects. Extensive experiments show that our
method achieves state-of-the-art performance on TextVQA and ST-VQA datasets.
More encouragingly, our model surpasses others by clear margins of 5.7\% and
12.1\% on questions that involve spatial reasoning in TextVQA and ST-VQA valid
split. Besides, we also verify the generalizability of our model on the
text-based image captioning task.
