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Zhengda Lu

4 accepted papers

2026

DehazeGS: Seeing Through Fog with 3D Gaussian Splatting

AAAI 2026technical

Current novel view synthesis methods are typically designed for high-quality and clean input images. However, in foggy scenes, scattering and attenuation can significantly degrade the quality of rendering. Although NeRF-based dehazing approaches have been developed, their reliance on deep fully conn

Cited by 0SourcePDFScholar
2026

Pose-Free Omnidirectional Gaussian Splatting for 360-Degree Videos with Consistent Depth Priors

CVPR 2026

Omnidirectional 3D Gaussian Splatting with panoramas is a key technique for 3D scene representation, and existing methods typically rely on slow SfM to provide camera poses and sparse points priors. In this work, we propose a pose-free omnidirectional 3DGS method, named PFGS360, that reconstructs 3D

Cited by 0SourcecodeScholar
2025

Empowering Vector Graphics with Consistently Arbitrary Viewing and View-dependent Visibility

CVPR 2025highlight

This work presents a novel text-to-vector graphics generation approach, Dream3DVG, allowing for arbitrary viewpoint viewing, progressive detail optimization, and view-dependent occlusion awareness. Our approach is a dual-branch optimization framework, consisting of an auxiliary 3D Gaussian Splattin…

2022

ACDNet: Adaptively Combined Dilated Convolution for Monocular Panorama Depth Estimation

AAAI 2022technical

Depth estimation is a crucial step for 3D reconstruction with panorama images in recent years. Panorama images maintain the complete spatial information but introduce distortion with equirectangular projection. In this paper, we propose an ACDNet based on the adaptively combined dilated convolution…