Hansheng Chen 陈涵晟

About me

I am a first-year PhD student at Stanford University. I am passionate about computer graphics and vision research, currently with a specific focus on 3D generation, reconstruction, and neural rendering. Previously, I worked on image-based 6DoF pose estimation.

I obtained my master’s and bachelor’s degree in Automotive Engineering in Tongji University. I have interned at SU Lab (working with Prof. Hao Su) and Alibaba DAMO Academy (working with Dr. Pichao Wang). During my undergrad I worked on Formula SAE engineering as a racing enthusiast.



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

Minghua Liu*, Ruoxi Shi*, Linghao Chen*, Zhuoyang Zhang*, Chao Xu*, Xinyue Wei, Hansheng Chen, Chong Zeng, Jiayuan Gu, Hao Su
CVPR, 2024

Combining multiview diffusion and multiview-conditioned 3D diffusion models, One-2-3-45++ is capable of transforming a single RGB image of any object into a high-fidelity textured mesh in under one minute.

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

Ruoxi Shi, Hansheng Chen, Zhuoyang Zhang, Minghua Liu, Chao Xu, Xinyue Wei, Linghao Chen, Chong Zeng, Hao Su
Technical report, 2023

Zero123++ transforms a single RGB image of any object into high-quality multiview images with superior 3D consistency, serving as a strong base model for image-to-3D generative tasks.

Single-Stage Diffusion NeRF: A Unified Approach to 3D Generation and Reconstruction

Hansheng Chen, Jiatao Gu, Anpei Chen, Wei Tian, Zhuowen Tu, Lingjie Liu, Hao Su
ICCV, 2023

With 3D diffusion models and NeRFs trained in a single stage, SSDNeRF learns powerful 3D generative prior from multi-view images, which can be exploited for unconditional generation and image-based 3D reconstruction.

EPro-PnP: Generalized End-to-End Probabilistic Perspective-n-Points for Monocular Object Pose Estimation

Hansheng Chen, Pichao Wang, Fan Wang, Wei Tian, Lu Xiong, Hao Li
CVPR, 2022 (Best Student Paper)

We present a probabilistic PnP layer for end-to-end 6DoF pose learning. The layer outputs the pose distribution with differentiable probability density, so that the 2D-3D correspondences can be learned flexibly by backpropagating the pose loss.

EPro-PnP: Generalized End-to-End Probabilistic Perspective-n-Points for Monocular Object Pose Estimation

Hansheng Chen, Wei Tian, Pichao Wang, Fan Wang, Lu Xiong, Hao Li
TPAMI, 2024

The updated paper features improved models with better results on both the LineMOD and nuScenes benchmark. Morever, we have added more discussions on the loss functions, which are supported by rigorous ablation studies.

MonoRUn: Monocular 3D Object Detection by Reconstruction and Uncertainty Propagation

Hansheng Chen, Yuyao Huang, Wei Tian, Zhong Gao, Lu Xiong
CVPR, 2021

We present a novel 3D object detection framework based on dense 2D-3D correspondences. An uncertainty-aware reprojection loss is proposed to learn the 3D coordinates without prior knowledge of the object geometry.

SPFCN: Select and Prune the Fully Convolutional Networks for Real-time Parking Slot Detection

Zhuoping Yu, Zhong Gao, Hansheng Chen, Yuyao Huang
IEEE Intelligent Vehicles Symposium (IV), 2020

A real time parking slot detection model that runs 30 FPS on a 2.3 GHz CPU core, yielding corner localization error of 1.51±2.14 cm (std. err.) and slot detection accuracy of 98%.