Lihe Ding

I am a first-year Ph.D. student from MMLab, The Chinese University of Hong Kong, and my advisor is Prof. Tianfan Xue. I received both my Bachelor's and Master's degrees from Beijing Institute of Technology, supervised by Prof. Jianan Li. I spent a wonderful time in Qcraft and SenseTime as a research intern, mentored by Boyin Zhang and Dr. Zhanpeng Huang, respectively.

My current research interest lies in 3D vision, especially for high-quality, scalable 3D Generation with Diffusion Model and NeRF.

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Email: dean.dinglihe at outlook dot com

I have been working on NeRF and Diffusion Model for 3D generation. My previous work also included 3D perception on Point Clouds. (*: Equal Contribution †: Corresponding Author)

Interactive3D grants users precise control over the generative process through extensive 3D interaction capabilities.

We intergrate both 3D and 2D diffusion models with powerful priors into a unified framework with bidirectional guidance.

We propose Obj-NeRF, a novel comprehensive pipeline to extract object NeRFs from multi-view images, which can be used for NeRF editing tasks.

we propose an alternative to make sample-adaptive transformations based on the structure of the sample to cope with potential corruption via an auto-augmentation framework.

We establish new lidar-scanned scene flow datasets and propose a fast and hierarchical network for real-world scene flow estimation.

We propose a novel class-aware 3D proposal generation strategy and an efficient fully sparse convolutional 3D refinement module for vote-based Indoor 3D Detection.

We propose the first powerful 3D window-based transformer backbone on sparse 3D voxels leveraging mixed-scale information.

We propose a novel multi-modality two-stage approach to effectively and efficiently fuse point clouds and camera images in the Regions of Interest(RoI).

The dynamic graph constructing strategy can well capture the fine-grained geometry ignored by previous aggregation operations.

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