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Recent studies on dense captioning and visual grounding in 3D have achievedimpressive results. Despite developments in both areas, the limited amount ofavailable 3D vision-language data causes overfitting issues for 3D visualgrounding and 3D dense captioning methods. Also, how to discriminativelydescribe objects in complex 3D environments is not fully studied yet. Toaddress these challenges, we present D3Net, an end-to-end neuralspeaker-listener architecture that can detect, describe and discriminate. OurD3Net unifies dense captioning and visual grounding in 3D in a self-criticalmanner. This self-critical property of D3Net also introduces discriminabilityduring object caption generation and enables semi-supervised training onScanNet data with partially annotated descriptions. Our method outperforms SOTAmethods in both tasks on the ScanRefer dataset, surpassing the SOTA 3D densecaptioning method by a significant margin.

D3Net: A Unified Speaker-Listener Architecture for 3D Dense Captioning and Visual Grounding