📝 Publications

Arxiv
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High-Fidelity Mask-free Neural Surface Reconstruction for Virtual Reality

Haotian Bai, Yize Chen, Lin Wang

Project | Video |

  • A novel rendering-based framework for neural implicit surface reconstruction, aiming to recover compact and precise surfaces without multi-view object masks.
  • Since overlaps in images implicitly identifies the surface that a user intends to capture, Hi-NeuS takes multi-view rendering weights to guide the signed distance functions of neural surfaces in a self-supervised manner.
Arxiv
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CompoNeRF: Text-guided Multi-object Compositional NeRF with Editable 3D Scene Layout

Haotian Bai, Yuanhuiyi Lyu, Lutao Jiang, Sijia Li, Haonan Lu, Xiaodong Lin, Lin Wang

Project | Video |

  • A novel framework that synthesizes coherent multi-object scenes by integrating textual descriptions with box-based spatial arrangements.
  • CompoNeRF is designed for precision and adaptability, allowing for individual NeRFs, each denoted by a unique prompt color, to be composed, decomposed, and recomposed with ease, streamlining the construction of complex scenes from cached models after decomposition.
ICCV 2023
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Dynamic PlenOctree for Adaptive Sampling Refinement in Explicit NeRF

Haotian Bai, Yiqi Lin, Yize Chen, Lin Wang

Project | Video |

  • A more compact and fertile PlenOctree (POT) NeRF representation.
  • Inspiration: POT’s fixed structure for direct optimization is sub-optimal as the scene complexity evolves continuously with updates to cached color and density, necessitating refining the sampling distribution to capture signal complexity accordingly.
  • Competitive: DOT outperforms POT by enhancing visual quality, reducing over 55.15/68.84% parameters, and providing 1.7/1.9 times FPS for NeRF-synthetic and Tanks and Temples.
CVPR 2023
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Patch-Mix Transformer for Unsupervised Domain Adaptation: A Game Perspective

Jinjing Zhu*, Haotian Bai*, Lin Wang

Project | Video |

  • Be selected as one of CVPR (highlight) papers(top 2.5%)
  • Large Domain Gap: PMTrans bridges source and target domains with an intermediate domain in a relatively smooth way.
  • Game Theory: Interpret UDA as a min-max CE game with three players, including the feature extractor, classifier, and PatchMix to find the Nash Equilibria.
  • Competitive: PMTrans surpasses ViT-based and CNN-based SoTA methods by +3.6% on Office-Home, +1.4% on Office-31, and +17.7% on DomainNet.
ECCV 2022
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Weakly Supervised Object Localization via Transformer with Implicit Spatial Calibration

Haotian Bai, Ruimao Zhang, Jiong Wang, Xiang Wan

Project | Video |

  • SCM is the external transformer based solution for Weakly Supervised Object Localization.
  • Lightweight: SCM is an external Transformer model that produces no additional parameters.
  • Competitive: SCM outperforms most competitive frameworks (CNN & Transformer) using only about 𝟐𝟎%~𝟑𝟎% of their parameters.