activity
20242026
collaborators

8 papers

cs.CV2026

CR-Refiner: An Object-Centric Optimal Transport Reranker for Edit-Conditioned 3D Scene Retrieval

Hao Wu, Jinjing Zhu, Nanyu Wu +4

Edit-conditioned 3D scene retrieval pairs a reference 3D room with a natural-language modification and retrieves rooms from a corpus that satisfy the edit. Three lines of prior wor…

cs.CV2026

EventVGGT: Exploring Cross-Modal Distillation for Consistent Event-based Depth Estimation

Yinrui Ren, Jinjing Zhu, Kanghao Chen +8

Event cameras offer superior sensitivity to high-speed motion and extreme lighting, making event-based monocular depth estimation a promising approach for robust 3D perception in c…

cs.CV2026

Sat2City v2: Native 3D City Asset Generation from a Single Satellite Image

Tongyan Hua, Dongli Wu, Jinjing Zhu +5

Generating explicit 3D city assets from a single satellite image is important for digital twins, urban simulation, and geospatial intelligence. Unlike satellite-to-street-view synt…

cs.CV2026

Beyond a Single Light: A Large-Scale Aerial Dataset for Urban Scene Reconstruction Under Varying Illumination

Zhuoxiao Li, Wenzong Ma, Taoyu Wu +8

Recent advances in Neural Radiance Fields and 3D Gaussian Splatting have demonstrated strong potential for large-scale UAV-based 3D reconstruction tasks by fitting the appearance o…

cs.CV2026

Holo360D: A Large-Scale Real-World Dataset with Continuous Trajectories for Advancing Panoramic 3D Reconstruction and Beyond

Jing Ou, Zidong Cao, Yinrui Ren +6

While feed-forward 3D reconstruction models have advanced rapidly, they still exhibit degraded performance on panoramas due to spherical distortions. Moreover, existing panoramic 3…

cs.CV2025

PanDA: Towards Panoramic Depth Anything with Unlabeled Panoramas and Mobius Spatial Augmentation

Zidong Cao, Jinjing Zhu, Weiming Zhang +4

Recently, Depth Anything Models (DAMs) - a type of depth foundation models - have demonstrated impressive zero-shot capabilities across diverse perspective images. Despite its succ…