Chiyu Max Jiang

Affiliations:
  • University of California, Berkeley, CA, USA (PhD 2020)


According to our database1, Chiyu Max Jiang authored at least 18 papers between 2017 and 2024.

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Bibliography

2024
3D Open-Vocabulary Panoptic Segmentation with 2D-3D Vision-Language Distillation.
CoRR, 2024

2023
Towards general-purpose representation learning of polygonal geometries.
GeoInformatica, April, 2023

OpenScene: 3D Scene Understanding with Open Vocabularies.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

MotionDiffuser: Controllable Multi-Agent Motion Prediction Using Diffusion.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

NeRDi: Single-View NeRF Synthesis with Language-Guided Diffusion as General Image Priors.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022
Improving the Intra-class Long-Tail in 3D Detection via Rare Example Mining.
Proceedings of the Computer Vision - ECCV 2022, 2022

2021
Shape As Points: A Differentiable Poisson Solver.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020
ShapeFlow: Learnable Deformations Among 3D Shapes.
CoRR, 2020

MeshODE: A Robust and Scalable Framework for Mesh Deformation.
CoRR, 2020

MeshfreeFlowNet: a physics-constrained deep continuous space-time super-resolution framework.
Proceedings of the International Conference for High Performance Computing, 2020

ShapeFlow: Learnable Deformation Flows Among 3D Shapes.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Local Implicit Grid Representations for 3D Scenes.
Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

Adversarial Texture Optimization From RGB-D Scans.
Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

2019
Leveraging Bayesian analysis to improve accuracy of approximate models.
J. Comput. Phys., 2019

Convolutional Neural Networks on Non-uniform Geometrical Signals Using Euclidean Spectral Transformation.
Proceedings of the 7th International Conference on Learning Representations, 2019

Spherical CNNs on Unstructured Grids.
Proceedings of the 7th International Conference on Learning Representations, 2019

DDSL: Deep Differentiable Simplex Layer for Learning Geometric Signals.
Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision, 2019

2017
Hierarchical Detail Enhancing Mesh-Based Shape Generation with 3D Generative Adversarial Network.
CoRR, 2017


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