Paper "novel view synthesis" Papers
17 papers found
Conference
Enhanced Velocity Field Modeling for Gaussian Video Reconstruction
Zhenyang Li, Xiaoyang Bai, Tongchen Zhang et al.
Enhancing Close-up Novel View Synthesis via Pseudo-labeling
Jiatong Xia, Libo Sun, Lingqiao Liu
FatesGS: Fast and Accurate Sparse-View Surface Reconstruction Using Gaussian Splatting with Depth-Feature Consistency
Han Huang, Yulun Wu, Chao Deng et al.
GGS: Generalizable Gaussian Splatting for Lane Switching in Autonomous Driving
Huasong Han, Kaixuan Zhou, Xiaoxiao Long et al.
GURecon: Learning Detailed 3D Geometric Uncertainties for Neural Surface Reconstruction
Zesong Yang, Ru Zhang, Jiale Shi et al.
IntelliCap: Intelligent Guidance for Consistent View Sampling
Ayaka Yasunaga, Hideo Saito, Dieter Schmalstieg et al.
KeyGS: A Keyframe-Centric Gaussian Splatting Method for Monocular Image Sequences
Keng-Wei Chang, Zi-Ming Wang, Shang-Hong Lai
Pragmatist: Multiview Conditional Diffusion Models for High-Fidelity 3D Reconstruction from Unposed Sparse Views
Songchun Zhang, Chunhui Zhao
SoundBrush: Sound as a Brush for Visual Scene Editing
Kim Sung-Bin, Kim Jun-Seong, Junseok Ko et al.
Spatial Annealing for Efficient Few-shot Neural Rendering
Yuru Xiao, Deming Zhai, Wenbo Zhao et al.
AltNeRF: Learning Robust Neural Radiance Field via Alternating Depth-Pose Optimization
Kun Wang, Zhiqiang Yan, Huang Tian et al.
BLiRF: Bandlimited Radiance Fields for Dynamic Scene Modeling
Sameera Ramasinghe, Violetta Shevchenko, Gil Avraham et al.
CF-NeRF: Camera Parameter Free Neural Radiance Fields with Incremental Learning
Qingsong Yan, Qiang Wang, Kaiyong Zhao et al.
ColNeRF: Collaboration for Generalizable Sparse Input Neural Radiance Field
Zhangkai Ni, Peiqi Yang, Wenhan Yang et al.
Few-Shot Neural Radiance Fields under Unconstrained Illumination
SeokYeong Lee, JunYong Choi, Seungryong Kim et al.
NeRF-LiDAR: Generating Realistic LiDAR Point Clouds with Neural Radiance Fields
Junge Zhang, Feihu Zhang, Shaochen Kuang et al.
SpectralNeRF: Physically Based Spectral Rendering with Neural Radiance Field
Ru Li, Jia Liu, Guanghui Liu et al.