8 papers
VDEGaussian: Video Diffusion Enhanced 4D Gaussian Splatting for Dynamic Urban Scenes Modeling
Yuru Xiao, Zihan Lin, Chao Lu +7
Dynamic urban scene modeling is a rapidly evolving area with broad applications. While current approaches leveraging neural radiance fields or Gaussian Splatting have achieved fine…
EchoReview: Learning Peer Review from the Echoes of Scientific Citations
Yinuo Zhang, Dingcheng Huang, Haifeng Suo +9
As the volume of scientific submissions continues to grow rapidly, traditional peer review systems are facing unprecedented scalability pressures, highlighting the urgent need for…
Variation-Bounded Loss for Noise-Tolerant Learning
Jialiang Wang, Xiong Zhou, Xianming Liu +4
Mitigating the negative impact of noisy labels has been aperennial issue in supervised learning. Robust loss functions have emerged as a prevalent solution to this problem. In this…
SGCNeRF: Few-Shot Neural Rendering via Sparse Geometric Consistency Guidance
Yuru Xiao, Xianming Liu, Deming Zhai +3
Neural Radiance Field (NeRF) technology has made significant strides in creating novel viewpoints. However, its effectiveness is hampered when working with sparsely available views…
MCGS: Multiview Consistency Enhancement for Sparse-View 3D Gaussian Radiance Fields
Yuru Xiao, Deming Zhai, Wenbo Zhao +3
Radiance fields represented by 3D Gaussians excel at synthesizing novel views, offering both high training efficiency and fast rendering. However, with sparse input views, the lack…
-Softmax: Approximating One-Hot Vectors for Mitigating Label Noise
Jialiang Wang, Xiong Zhou, Deming Zhai +3
Noisy labels pose a common challenge for training accurate deep neural networks. To mitigate label noise, prior studies have proposed various robust loss functions to achieve noise…