11 citations · 13 across the 5 of their papers we have counts for
5 papers
Vote2Cap-DETR++: Decoupling Localization and Describing for End-to-End 3D Dense Captioning
Sijin Chen, Hongyuan Zhu, Mingsheng Li +6
3D dense captioning requires a model to translate its understanding of an input 3D scene into several captions associated with different object regions. Existing methods adopt a so…
Michelangelo: Conditional 3D Shape Generation based on Shape-Image-Text Aligned Latent Representation
Zibo Zhao, Wen Liu, Xin Chen +7
We present a novel alignment-before-generation approach to tackle the challenging task of generating general 3D shapes based on 2D images or texts. Directly learning a conditional…
Multi-view Vision-Prompt Fusion Network: Can 2D Pre-trained Model Boost 3D Point Cloud Data-scarce Learning?
Haoyang Peng, Baopu Li, Bo Zhang +3
Point cloud based 3D deep model has wide applications in many applications such as autonomous driving, house robot, and so on. Inspired by the recent prompt learning in natural lan…
A Large-Scale Outdoor Multi-modal Dataset and Benchmark for Novel View Synthesis and Implicit Scene Reconstruction
Chongshan Lu, Fukun Yin, Xin Chen +3
Neural Radiance Fields (NeRF) has achieved impressive results in single object scene reconstruction and novel view synthesis, which have been demonstrated on many single modality a…
End-to-End 3D Dense Captioning with Vote2Cap-DETR
Sijin Chen, Hongyuan Zhu, Xin Chen +3
3D dense captioning aims to generate multiple captions localized with their associated object regions. Existing methods follow a sophisticated ``detect-then-describe'' pipeline equ…