1 citations · 1 across the 6 of their papers we have counts for
6 papers
ForeSplat: Optimization-Aware Foresight for Feed-Forward 3D Gaussian Splatting
Yuke Li, Weihang Liu, Cheng Zhang +8
Feed-forward 3D Gaussian Splatting models offer fast single-pass reconstruction,but scaling them to match per-scene optimization quality is fundamentally hindered by the scarcity o…
Duplex-GS: Proxy-Guided Weighted Blending for Real-Time Order-Independent Gaussian Splatting
Weihang Liu, Yuke Li, Yuxuan Li +2
Recent advances in 3D Gaussian Splatting (3DGS) have demonstrated remarkable rendering fidelity and efficiency. However, these methods still rely on computationally expensive seque…
Adaptive Time-step Training for Enhancing Spike-Based Neural Radiance Fields
Ranxi Lin, Canming Yao, Jiayi Li +3
Spiking Neural Networks (SNNs) provide an energy-efficient computing paradigm for neural rendering, but existing spike-based Neural Radiance Field (NeRF) models usually use a fixed…
CityGo: Lightweight Urban Modeling and Rendering with Proxy Buildings and Residual Gaussians
Weihang Liu, Yuhui Zhong, Yuke Li +8
Accurate and efficient modeling of large-scale urban scenes is critical for applications such as AR navigation, UAV based inspection, and smart city digital twins. While aerial ima…
Quantitative Error Feedback for Quantization Noise Reduction of Filtering over Graphs
Xue Xian Zheng, Weihang Liu, Xin Lou +2
This paper introduces an innovative error feedback framework designed to mitigate quantization noise in distributed graph filtering, where communications are constrained to quantiz…
Content-Aware Radiance Fields: Aligning Model Complexity with Scene Intricacy Through Learned Bitwidth Quantization
Weihang Liu, Xue Xian Zheng, Jingyi Yu +1
The recent popular radiance field models, exemplified by Neural Radiance Fields (NeRF), Instant-NGP and 3D Gaussian Splatting, are designed to represent 3D content by that training…