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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…
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…