5 papers
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…
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…
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…
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…
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…