8 citations · 9 across the 15 of their papers we have counts for
15 papers
AnyGS2Mesh: Feed-Forward Mesh Reconstruction from 3D Gaussian Splatting with Arbitrary-Resolution Views
Yuxuan Song, Fan Gao, Yibo Zhao +3
Existing 3D mesh reconstruction methods from Gaussian scene representations predominantly rely on iterative optimization, resulting in slow inference and limited scalability to hig…
LightBridge: Feed-Forward Generative Relighting for 3D Gaussian Splatting
Hezhi Cao, Panhao Cheng, huangsheng du +3
3D Gaussian Splatting (3DGS) achieves high-quality, real-time novel view synthesis, but the resulting assets have baked-in illumination and cannot be easily relit. Inverse renderin…
Towards Valid B-Rep Generation: Training-Free Wireframe Anomaly Detection and Repair
Jingyu Wu, Youcheng Cai, Tengyu Luo +1
Multi-stage boundary representation (B-Rep) generation leverages intermediate wireframes to synthesize CAD models. However, geometric and topological risks in these wireframes -- s…
StreamSplat: Streaming Feed-Forward 3D Gaussian Splatting
Changhao Song, Yuxuan Wang, Qibiao Li +2
Feed-forward 3D Gaussian Splatting enables efficient novel-view synthesis without per-scene optimization, but most existing methods assume a fixed set of context views and process…
CORGI: Consistency-Aware 3D Dog Reconstruction from a Single Image in the Wild
Yuxiao Wu, Weile Li, Boyi Zhu +3
Reconstructing high-fidelity 3D models of highly articulated animals, such as dogs, from a single in-the-wild image remains a formidable challenge. In this paper, we introduce CORG…
Mesh2GS: White-Box 3DGS Construction via Plenoptic Sampling
Haoran Zhu, Youcheng Cai, Huangsheng Du +2
3D Gaussian Splatting (3DGS) has emerged as a promising method for high-quality, real-time 3D reconstruction. To associate 3DGS with mesh representations, existing methods primaril…
RenderFormer++: Scalable and Physics-Informed Feed-Forward Neural Rendering
Huangsheng Du, Haoran Zhu, Youcheng Cai +2
We present RenderFormer++, a scalable and physics-informed feed-forward neural rendering framework for global illumination in mesh scenes. Existing Transformer-based neural renderi…
3DGS: Joint Super Sampling and Frame Interpolation for Real-Time Large-Scale 3DGS Rendering
Yibo Zhao, Fan Gao, Youcheng Cai +1
3D Gaussian Splatting (3DGS) enables high-quality real-time 3D rendering but faces challenges in efficiently scaling to ultra-dense scenes and high-resolution due to computational…
VVGT: Visual Volume-Grounded Transformer
Yuxuan Wang, Qibiao Li, Youcheng Cai
Volumetric visualization has long been dominated by Direct Volume Rendering (DVR), which operates on dense voxel grids and suffers from limited scalability as resolution and intera…
STAC: Plug-and-Play Spatio-Temporal Aware Cache Compression for Streaming 3D Reconstruction
Runze Wang, Yuxuan Song, Youcheng Cai +1
Online 3D reconstruction from streaming inputs requires both long-term temporal consistency and efficient memory usage. Although causal variants of VGGT address this challenge thro…
Variable Basis Mapping for Real-Time Volumetric Visualization
Qibiao Li, Yuxuan Wang, Youcheng Cai +2
Real-time visualization of large-scale volumetric data remains challenging, as direct volume rendering and voxel-based methods suffer from prohibitively high computational cost. We…
HybridWorldSim: A Scalable and Controllable High-fidelity Simulator for Autonomous Driving
Qiang Li, Yingwenqi Jiang, Tuoxi Li +17
Realistic and controllable simulation is critical for advancing end-to-end autonomous driving, yet existing approaches often struggle to support novel view synthesis under large vi…
Plug-and-Play PDE Optimization for 3D Gaussian Splatting: Toward High-Quality Rendering and Reconstruction
Yifan Mo, Youcheng Cai, Ligang Liu
3D Gaussian Splatting (3DGS) has revolutionized radiance field reconstruction by achieving high-quality novel view synthesis with fast rendering speed, introducing 3D Gaussian prim…
CraftMesh: High-Fidelity Generative Mesh Manipulation via Poisson Seamless Fusion
James Jincheng, Yuxiao Wu, Youcheng Cai +1
Controllable, high-fidelity mesh editing remains a significant challenge in 3D content creation. Existing generative methods often struggle with complex geometries and fail to prod…
AIM 2024 Sparse Neural Rendering Challenge: Methods and Results
Michal Nazarczuk, Sibi Catley-Chandar, Thomas Tanay +27
This paper reviews the challenge on Sparse Neural Rendering that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ECCV 2024. This manuscript…