9 citations · 13 across the 6 of their papers we have counts for
6 papers
CDI3D: Cross-guided Dense-view Interpolation for 3D Reconstruction
Zhiyuan Wu, Xibin Song, Senbo Wang +8
3D object reconstruction from single-view image is a fundamental task in computer vision with wide-ranging applications. Recent advancements in Large Reconstruction Models (LRMs) h…
Pandora3D: A Comprehensive Framework for High-Quality 3D Shape and Texture Generation
Jiayu Yang, Taizhang Shang, Weixuan Sun +7
This report presents a comprehensive framework for generating high-quality 3D shapes and textures from diverse input prompts, including single images, multi-view images, and text d…
MARS: Mesh AutoRegressive Model for 3D Shape Detailization
Jingnan Gao, Weizhe Liu, Weixuan Sun +8
State-of-the-art methods for mesh detailization predominantly utilize Generative Adversarial Networks (GANs) to generate detailed meshes from coarse ones. These methods typically l…
LAM3D: Large Image-Point-Cloud Alignment Model for 3D Reconstruction from Single Image
Ruikai Cui, Xibin Song, Weixuan Sun +8
Large Reconstruction Models have made significant strides in the realm of automated 3D content generation from single or multiple input images. Despite their success, these models…
NeuSDFusion: A Spatial-Aware Generative Model for 3D Shape Completion, Reconstruction, and Generation
Ruikai Cui, Weizhe Liu, Weixuan Sun +9
3D shape generation aims to produce innovative 3D content adhering to specific conditions and constraints. Existing methods often decompose 3D shapes into a sequence of localized c…
Frankenstein: Generating Semantic-Compositional 3D Scenes in One Tri-Plane
Han Yan, Yang Li, Zhennan Wu +9
We present Frankenstein, a diffusion-based framework that can generate semantic-compositional 3D scenes in a single pass. Unlike existing methods that output a single, unified 3D s…