Visual Question Answering (VQA) is a widely studied problem in computer vision and natural language processing. However, current approaches to VQA have been investigated primarily in the 2D image domain. We study VQA in the 3D domain, with our input being point clouds of realworld 3D scenes, instead of 2D images. We believe that this 3D data modality provide richer spatial relation information that is of interest in the VQA task. In this paper, we introduce the 3DVQA-ScanNet dataset, the first VQA dataset in 3D, and we investigate the performance of a spectrum of baseline approaches on the 3D VQA task.
Dataset statistics for our 3DVQA dataset.
Illustration of the network structure for our VoteNet + LSTM.
Below one can download the full and sampled versions of 3DVQA datasetcoming soon!