no code implementations • 28 Mar 2024 • Guangyu Wang, Jinzhi Zhang, Fan Wang, Ruqi Huang, Lu Fang
We also introduce a novel dataset, namely GigaNVS, to benchmark cross-scale, high-resolution novel view synthesis of realworld large-scale scenes.
1 code implementation • 20 Nov 2023 • Haiyang Ying, Yixuan Yin, Jinzhi Zhang, Fan Wang, Tao Yu, Ruqi Huang, Lu Fang
Towards holistic understanding of 3D scenes, a general 3D segmentation method is needed that can segment diverse objects without restrictions on object quantity or categories, while also reflecting the inherent hierarchical structure.
no code implementations • ICCV 2023 • Haiyang Ying, Baowei Jiang, Jinzhi Zhang, Di Xu, Tao Yu, Qionghai Dai, Lu Fang
This paper proposes a method for fast scene radiance field reconstruction with strong novel view synthesis performance and convenient scene editing functionality.
no code implementations • 6 Jul 2023 • Yijie Deng, Lei Han, Tianpeng Lin, Lin Li, Jinzhi Zhang, Lu Fang
Based on this insight, we introduce EffLiFe, a novel light field optimization method, which leverages the proposed Hierarchical Sparse Gradient Descent (HSGD) to produce high-quality light fields from sparse view images in real time.
no code implementations • ICCV 2023 • Haozhe Lin, Zequn Chen, Jinzhi Zhang, Bing Bai, Yu Wang, Ruqi Huang, Lu Fang
The CGG task capitalizes on the calibrated multiview videos of a dynamic scene, and targets at recovering semantic information (coordination, trajectories and relationships) of the presented objects in the form of spatio-temporal context graph in 4D space.
1 code implementation • 26 May 2020 • Mengqi Ji, Jinzhi Zhang, Qionghai Dai, Lu Fang
Furthermore, the latter problem is handled via a multi-scale strategy that consequently refines the recovered geometry around the region with the repeating pattern.
no code implementations • 24 Aug 2018 • Yi-Lin Tuan, Jinzhi Zhang, Yujia Li, Hung-Yi Lee
In sequence generation task, many works use policy gradient for model optimization to tackle the intractable backpropagation issue when maximizing the non-differentiable evaluation metrics or fooling the discriminator in adversarial learning.