5 papers
CEI-3D: Collaborative Explicit-Implicit 3D Reconstruction for Realistic and Fine-Grained Object Editing
Yue Shi, Rui Shi, Yuxuan Xiong +2
Existing 3D editing methods often produce unrealistic and unrefined results due to the deeply integrated nature of their reconstruction networks. To address the challenge, this pap…
HPR3D: Hierarchical Proxy Representation for High-Fidelity 3D Reconstruction and Controllable Editing
Tielong Wang, Yuxuan Xiong, Jinfan Liu +4
Current 3D representations like meshes, voxels, point clouds, and NeRF-based neural implicit fields exhibit significant limitations: they are often task-specific, lacking universal…
InstantSticker: Realistic Decal Blending via Disentangled Object Reconstruction
Yi Zhang, Xiaoyang Huang, Yishun Dou +5
We present InstantSticker, a disentangled reconstruction pipeline based on Image-Based Lighting (IBL), which focuses on highly realistic decal blending, simulates stickers attached…
DualNeRF: Text-Driven 3D Scene Editing via Dual-Field Representation
Yuxuan Xiong, Yue Shi, Yishun Dou +1
Recently, denoising diffusion models have achieved promising results in 2D image generation and editing. Instruct-NeRF2NeRF (IN2N) introduces the success of diffusion into 3D scene…
DARF: Depth-Aware Generalizable Neural Radiance Field
Yue Shi, Dingyi Rong, Chang Chen +3
Neural Radiance Field (NeRF) has revolutionized novel-view rendering tasks and achieved impressive results. However, the inefficient sampling and per-scene optimization hinder its…