4K-NeRF: High Fidelity Neural Radiance Fields at Ultra High Resolutions

1 code implementation • 9 Dec 2022 • Zhongshu Wang, Lingzhi Li, Zhen Shen, Li Shen, Liefeng Bo

In this paper, we present a novel and effective framework, named 4K-NeRF, to pursue high fidelity view synthesis on the challenging scenarios of ultra high resolutions, building on the methodology of neural radiance fields (NeRF).

Compressing Volumetric Radiance Fields to 1 MB

1 code implementation • 29 Nov 2022 • Lingzhi Li, Zhen Shen, Zhongshu Wang, Li Shen, Liefeng Bo

Approximating radiance fields with volumetric grids is one of promising directions for improving NeRF, represented by methods like Plenoxels and DVGO, which achieve super-fast training convergence and real-time rendering.

Model Compression Neural Rendering +1

Streaming Radiance Fields for 3D Video Synthesis

1 code implementation • 26 Oct 2022 • Lingzhi Li, Zhen Shen, Zhongshu Wang, Li Shen, Ping Tan

Instead of training a single model that combines all the frames, we formulate the dynamic modeling problem with an incremental learning paradigm in which per-frame model difference is trained to complement the adaption of a base model on the current frame.

Incremental Learning Model Optimization +1

GraphFit: Learning Multi-scale Graph-Convolutional Representation for Point Cloud Normal Estimation

1 code implementation • 23 Jul 2022 • Keqiang Li, Mingyang Zhao, Huaiyu Wu, Dong-Ming Yan, Zhen Shen, Fei-Yue Wang, Gang Xiong

We propose a precise and efficient normal estimation method that can deal with noise and nonuniform density for unstructured 3D point clouds.

Surface Normals Estimation

AsymmNet: Towards ultralight convolution neural networks using asymmetrical bottlenecks

1 code implementation • 15 Apr 2021 • Haojin Yang, Zhen Shen, Yucheng Zhao

Deep convolutional neural networks (CNN) have achieved astonishing results in a large variety of applications.

Image Classification