ZHENNAN WU — accepted AI-conference papers

2024

"NeuSDFusion: A Spatial-Aware Generative Model for 3D Shape Completion, Reconstruction, and Generation"

Ruikai Cui, Weizhe Liu*, Weixuan Sun, Senbo Wang, Taizhang Shang, Yang Li +6

ECCV 2024poster

"3D shape generation aims to produce innovative 3D content adhering to specific conditions and constraints. Existing methods often decompose 3D shapes into a sequence of localized components, treating each element in isolation without considering spatial consistency. As a result, these approaches ex…

Cited by 8 Source ↗PDF ↗code ↗Scholar ↗

2023

3D Segmenter: 3D Transformer based Semantic Segmentation via 2D Panoramic Distillation

ZHENNAN WU, YANG LI, Yifei Huang, Lin Gu, Tatsuya Harada, Hiroyuki Sato

ICLR 2023poster

Recently, 2D semantic segmentation has witnessed a significant advancement thanks to the huge amount of 2D image datasets available. Therefore, in this work, we propose the first 2D-to-3D knowledge distillation strategy to enhance 3D semantic segmentation model with knowledge embedded in the latent…

Cited by 4 Source ↗PDF ↗Scholar ↗