End-to-End 3D Dense Captioning with Vote2Cap-DETR
This addresses the problem of generating localized captions for 3D scenes, offering a simpler and more effective solution for computer vision researchers, though it is incremental as it builds on existing DETR methods.
The paper tackles 3D dense captioning by proposing Vote2Cap-DETR, a transformer-based framework that performs detection and captioning in one stage, achieving state-of-the-art improvements of 11.13% and 7.11% in CIDEr@0.5IoU on ScanRefer and Nr3D datasets.
3D dense captioning aims to generate multiple captions localized with their associated object regions. Existing methods follow a sophisticated ``detect-then-describe'' pipeline equipped with numerous hand-crafted components. However, these hand-crafted components would yield suboptimal performance given cluttered object spatial and class distributions among different scenes. In this paper, we propose a simple-yet-effective transformer framework Vote2Cap-DETR based on recent popular \textbf{DE}tection \textbf{TR}ansformer (DETR). Compared with prior arts, our framework has several appealing advantages: 1) Without resorting to numerous hand-crafted components, our method is based on a full transformer encoder-decoder architecture with a learnable vote query driven object decoder, and a caption decoder that produces the dense captions in a set-prediction manner. 2) In contrast to the two-stage scheme, our method can perform detection and captioning in one-stage. 3) Without bells and whistles, extensive experiments on two commonly used datasets, ScanRefer and Nr3D, demonstrate that our Vote2Cap-DETR surpasses current state-of-the-arts by 11.13\% and 7.11\% in CIDEr@0.5IoU, respectively. Codes will be released soon.