16 citations · 63 across the 10 of their papers we have counts for
10 papers
3DShape2VecSet: A 3D Shape Representation for Neural Fields and Generative Diffusion Models
Biao Zhang, Jiapeng Tang, Matthias Niessner +1
We introduce 3DShape2VecSet, a novel shape representation for neural fields designed for generative diffusion models. Our shape representation can encode 3D shapes given as surface…
RGBD2: Generative Scene Synthesis via Incremental View Inpainting using RGBD Diffusion Models
Jiabao Lei, Jiapeng Tang, Kui Jia
We address the challenge of recovering an underlying scene geometry and colors from a sparse set of RGBD view observations. In this work, we present a new solution termed RGBD…
Neural Shape Deformation Priors
Jiapeng Tang, Lev Markhasin, Bi Wang +2
We present Neural Shape Deformation Priors, a novel method for shape manipulation that predicts mesh deformations of non-rigid objects from user-provided handle movements. State-of…
PVSeRF: Joint Pixel-, Voxel- and Surface-Aligned Radiance Field for Single-Image Novel View Synthesis
Xianggang Yu, Jiapeng Tang, Yipeng Qin +4
We present PVSeRF, a learning framework that reconstructs neural radiance fields from single-view RGB images, for novel view synthesis. Previous solutions, such as pixelNeRF, rely…
OctField: Hierarchical Implicit Functions for 3D Modeling
Jia-Heng Tang, Weikai Chen, Jie Yang +4
Recent advances in localized implicit functions have enabled neural implicit representation to be scalable to large scenes. However, the regular subdivision of 3D space employed by…
SA-ConvONet: Sign-Agnostic Optimization of Convolutional Occupancy Networks
Jiapeng Tang, Jiabao Lei, Dan Xu +3
Surface reconstruction from point clouds is a fundamental problem in the computer vision and graphics community. Recent state-of-the-arts solve this problem by individually optimiz…
Learning Parallel Dense Correspondence from Spatio-Temporal Descriptors for Efficient and Robust 4D Reconstruction
Jiapeng Tang, Dan Xu, Kui Jia +1
This paper focuses on the task of 4D shape reconstruction from a sequence of point clouds. Despite the recent success achieved by extending deep implicit representations into 4D sp…
SkeletonNet: A Topology-Preserving Solution for Learning Mesh Reconstruction of Object Surfaces from RGB Images
Jiapeng Tang, Xiaoguang Han, Mingkui Tan +2
This paper focuses on the challenging task of learning 3D object surface reconstructions from RGB images. Existingmethods achieve varying degrees of success by using different surf…
Deep Mesh Reconstruction from Single RGB Images via Topology Modification Networks
Junyi Pan, Xiaoguang Han, Weikai Chen +2
Reconstructing the 3D mesh of a general object from a single image is now possible thanks to the latest advances of deep learning technologies. However, due to the nontrivial diffi…
A Skeleton-bridged Deep Learning Approach for Generating Meshes of Complex Topologies from Single RGB Images
Jiapeng Tang, Xiaoguang Han, Junyi Pan +2
This paper focuses on the challenging task of learning 3D object surface reconstructions from single RGB images. Existing methods achieve varying degrees of success by using differ…