1 citations · 1 across the 10 of their papers we have counts for
11 papers
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…
Sat2City v2: Native 3D City Asset Generation from a Single Satellite Image
Tongyan Hua, Dongli Wu, Jinjing Zhu +5
Generating explicit textured 3D city assets from a single satellite image is important for urban simulation and digital twins. Most prior methods, however, learn 3D proxies optimiz…
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…
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…
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…
Rethinking Knowledge in Distillation: An In-context Sample Retrieval Perspective
Jinjing Zhu, Songze Li, Lin Wang
Conventional knowledge distillation (KD) approaches are designed for the student model to predict similar output as the teacher model for each sample. Unfortunately, the relationsh…
CLIP the Divergence: Language-guided Unsupervised Domain Adaptation
Jinjing Zhu, Yucheng Chen, Lin Wang
Unsupervised domain adaption (UDA) has emerged as a popular solution to tackle the divergence between the labeled source and unlabeled target domains. Recently, some research effor…
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…
Energy-based Domain-Adaptive Segmentation with Depth Guidance
Jinjing Zhu, Zhedong Hu, Tae-Kyun Kim +1
Recent endeavors have been made to leverage self-supervised depth estimation as guidance in unsupervised domain adaptation (UDA) for semantic segmentation. Prior arts, however, ove…
Source-Free Cross-Modal Knowledge Transfer by Unleashing the Potential of Task-Irrelevant Data
Jinjing Zhu, Yucheng Chen, Lin Wang
Source-free cross-modal knowledge transfer is a crucial yet challenging task, which aims to transfer knowledge from one source modality (e.g., RGB) to the target modality (e.g., de…
Towards Dynamic and Small Objects Refinement for Unsupervised Domain Adaptative Nighttime Semantic Segmentation
Jingyi Pan, Sihang Li, Yucheng Chen +2
Nighttime semantic segmentation plays a crucial role in practical applications, such as autonomous driving, where it frequently encounters difficulties caused by inadequate illumin…