Junsheng Zhou | 周俊昇

I am currently a Ph.D. student in School of Software, Tsinghua University, advised by Prof. Yu-Shen Liu.

My research interests lie in the area of 3D computer vision and grapics, especially in 3D reconstruction, generative models, 3D foundation models and cross-modal learning.

Email  /  Google Scholar  /  GitHub

• 09/2024: DiffGS, DeepPriorAssembly and Binocular3DGS are accepted to NeurIPS 2024.
• 06/2024: Our paper FastN2N on fast learning of implicit 3D representations is accepted to TPAMI 2024.
• 04/2024: The extension of CAP-UDF on implicit representations is accepted to TPAMI 2024.
• 03/2024: Our paper UDiFF on UDF-based 3D generative models is accepted to CVPR 2024.
• 03/2024: I will co-organize workshop EMbodied AI: Trends, Challenges, and Opportunities in ICIP 2024.
• 02/2024: Our paper 3D-OAE on 3D foundation models is accepted to ICRA 2024 for Oral presentation.
• 01/2024: Our work Uni3D on scaling up 3D foundation models is accepted to ICLR 2024 (Spotlight).
• 12/2023: Two papers on multi-view reconstruction and point upsampling is accepted to AAAI 2024.
• 10/2023: Releasing Uni3D, a unified 3D foundation model with one billion parameters.
• 09/2023: Our paper VP2P-Match on image to LiDAR point cloud registration is accepted to NeurIPS 2023 (Spotlight).
• 08/2023: Our paper LevelSetUDF on neural implicit representations is accepted to ICCV 2023.
• 03/2023: Our paper LSA-SDF on implicit representation is accepted to CVPR 2023.
• 02/2023: Invited to give a talk about implicit surface reconstruction (CAP-UDF) at AI TIME (Video).
• 11/2022: Our paper NeAF on implicit point normal estimation is accepted to AAAI 2023 (Oral).
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09/2022: Our paper CAP-UDF on surface reconstruction is accepted to NeurIPS 2022.
03/2022: Our paper 3DAttriFlow on point cloud generation is accepted to CVPR 2022.
11/2021: My team won the 3rd Place in the MVP Completion Challenge (ICCV 2021 Workshop).
10/2021: Our paper MSM-Vis on data visualization won the Best Poster Paper on ChinaVR 2021.
09/2021: Started my journey at Tsinghua University.

(* Equal Contribution)

Introducing a powerful 3D generative model that generates Gaussian primitives in arbitrary numbers by functionally disentangling Gaussian Splatting.

We propose to assemble diverse deep priors from large models for scene generation from single images in a zero shot manner.

We propose a sparse view 3D Gaussian Splatting framework which explores the self supervisions inherent in the binocular stereo consistency.

We present a fast learning framework capable of inferring signed distance functions from noisy shapes within one minute through noise-to-noise mapping.

UDiFF is a 3D diffusion model for unsigned distance fields (UDFs) which is capable to generate textured 3D shapes with open surfaces from text conditions or unconditionally.

We present CAP-UDF to represent shapes and scenes with arbitrary architecture by learning a Consistency-Aware unsigned distance function Progressively.

We present Uni3D, a unified and scalable 3D pretraining framework for large-scale 3D representation learning, and explore its limits at the scale of one billion parameters.

We present 3D-OAE, a novel self-supervised point cloud representation learning framework which is highly efficient and can be further transferred to various downstream tasks.

A research project on 3D vision at Jilin University that I served as the advisor.

We present a 3D generation method that incorporates explicit generalized 3D priors with 2D diffusion priors to enhance the capability of generating unambiguous 3D consistent geometries.

We propose to learn a Local Distance Indicator as local priors to guide arbitrary-scale point cloud upsampling.

We design a sparse view reconstruction framework that leverages on-surface priors from explicit points to achieve highly faithful surface reconstruction.

We design a triplet network to learn VoxelPoint-to-Pixel matching via a differentiable probabilistic PnP solver.

We propose to guide the learning of zero level set in UDF using the rest non-zero level sets via a projection procedure.

We propose a level set alignment loss to evaluate the parallelism of level sets, which can be minimized to achieve better gradient consistency and eliminate uncertainty in the field.

A research project on 3D vision at Jilin University that I served as the advisor.

We present NeAF, a novel schema to learn implicit angle fields for point normal estimation. NeAF extends the success of implicit functions (e.g. NeRF and DeepSDF) to normal estimation.

We present CAP-UDF to represent shapes and scenes with arbitrary architecture by learning a Consistency-Aware unsigned distance function Progressively.

We present 3DAttriFlow to obtain high-quality 2D-to-3D reconstruction and point cloud completion results, while allowing controlled semantic attribute editing.

• National Scholarship (国家奖学金, Top 1% at Tsinghua University), 2023.
• The 3rd Place in the MVP Completion Challenge (ICCV 2021 Workshop), 2021.
• Best Poster Paper Awards on ChinaVR, 2021.

• Co-organizer: "EMbodied AI: Trends, Challenges, and Opportunities" in ICIP-24
• Program Committee Member: ICLR-24, IJCAI-24, WWW-24, AAAI-25
• Conference Reviewer: NeurIPS-23/24, ICML-24, CVPR-23/24, ICCV-23, SIGGRAPH Asia-24, ECCV-24, BMVC-23/24, AISTATS-25
• Journal Reviewer: TOG, TVCG, TIP, CVMJ

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Last updated: Oct 2024