Search Results for author: Minghua Liu

Found 16 papers, 11 papers with code

One-2-3-45++: Fast Single Image to 3D Objects with Consistent Multi-View Generation and 3D Diffusion

no code implementations14 Nov 2023 Minghua Liu, Ruoxi Shi, Linghao Chen, Zhuoyang Zhang, Chao Xu, Xinyue Wei, Hansheng Chen, Chong Zeng, Jiayuan Gu, Hao Su

Recent advancements in open-world 3D object generation have been remarkable, with image-to-3D methods offering superior fine-grained control over their text-to-3D counterparts.

Image Generation Image to 3D +1

Zero123++: a Single Image to Consistent Multi-view Diffusion Base Model

1 code implementation23 Oct 2023 Ruoxi Shi, Hansheng Chen, Zhuoyang Zhang, Minghua Liu, Chao Xu, Xinyue Wei, Linghao Chen, Chong Zeng, Hao Su

We report Zero123++, an image-conditioned diffusion model for generating 3D-consistent multi-view images from a single input view.

Distilling Large Vision-Language Model with Out-of-Distribution Generalizability

1 code implementation ICCV 2023 Xuanlin Li, Yunhao Fang, Minghua Liu, Zhan Ling, Zhuowen Tu, Hao Su

Model distillation, the process of creating smaller, faster models that maintain the performance of larger models, is a promising direction towards the solution.

Few-Shot Image Classification Knowledge Distillation +7

OpenShape: Scaling Up 3D Shape Representation Towards Open-World Understanding

1 code implementation NeurIPS 2023 Minghua Liu, Ruoxi Shi, Kaiming Kuang, Yinhao Zhu, Xuanlin Li, Shizhong Han, Hong Cai, Fatih Porikli, Hao Su

Due to their alignment with CLIP embeddings, our learned shape representations can also be integrated with off-the-shelf CLIP-based models for various applications, such as point cloud captioning and point cloud-conditioned image generation.

3D Classification 3D Shape Representation +4

Frame Mining: a Free Lunch for Learning Robotic Manipulation from 3D Point Clouds

1 code implementation14 Oct 2022 Minghua Liu, Xuanlin Li, Zhan Ling, Yangyan Li, Hao Su

We study how choices of input point cloud coordinate frames impact learning of manipulation skills from 3D point clouds.

3D Point Cloud Reinforcement Learning Imitation Learning +2

LESS: Label-Efficient Semantic Segmentation for LiDAR Point Clouds

no code implementations14 Oct 2022 Minghua Liu, Yin Zhou, Charles R. Qi, Boqing Gong, Hao Su, Dragomir Anguelov

Our method co-designs an efficient labeling process with semi/weakly supervised learning and is applicable to nearly any 3D semantic segmentation backbones.

3D Semantic Segmentation Autonomous Driving +3

Approximate Convex Decomposition for 3D Meshes with Collision-Aware Concavity and Tree Search

1 code implementation5 May 2022 Xinyue Wei, Minghua Liu, Zhan Ling, Hao Su

Approximate convex decomposition aims to decompose a 3D shape into a set of almost convex components, whose convex hulls can then be used to represent the input shape.

DeepMetaHandles: Learning Deformation Meta-Handles of 3D Meshes with Biharmonic Coordinates

1 code implementation CVPR 2021 Minghua Liu, Minhyuk Sung, Radomir Mech, Hao Su

Given a collection of 3D meshes of a category and their deformation handles (control points), our method learns a set of meta-handles for each shape, which are represented as combinations of the given handles.

SAPIEN: A SimulAted Part-based Interactive ENvironment

1 code implementation CVPR 2020 Fanbo Xiang, Yuzhe Qin, Kaichun Mo, Yikuan Xia, Hao Zhu, Fangchen Liu, Minghua Liu, Hanxiao Jiang, Yifu Yuan, He Wang, Li Yi, Angel X. Chang, Leonidas J. Guibas, Hao Su

To achieve this task, a simulated environment with physically realistic simulation, sufficient articulated objects, and transferability to the real robot is indispensable.


Morphing and Sampling Network for Dense Point Cloud Completion

2 code implementations30 Nov 2019 Minghua Liu, Lu Sheng, Sheng Yang, Jing Shao, Shi-Min Hu

3D point cloud completion, the task of inferring the complete geometric shape from a partial point cloud, has been attracting attention in the community.

Point Cloud Completion

Pre-training as Batch Meta Reinforcement Learning with tiMe

no code implementations25 Sep 2019 Quan Vuong, Shuang Liu, Minghua Liu, Kamil Ciosek, Hao Su, Henrik Iskov Christensen

Combining ideas from Batch RL and Meta RL, we propose tiMe, which learns distillation of multiple value functions and MDP embeddings from only existing data.

Meta Reinforcement Learning reinforcement-learning +1

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