7 papers
Weakly Supervised Cross-Modal Learning for 4D Radar Scene Flow Estimation
Jingyun Fu, Zhiyu Xiang, Na Zhao
Due to the difficulty of obtaining ground-truth data for 4D radar scene flow estimation, previous methods typically rely on either self-supervised losses or cross-modal supervision…
CCF: Complementary Collaborative Fusion for Domain Generalized Multi-Modal 3D Object Detection
Yuchen Wu, Kun Wang, Yining Pan +1
Multi-modal fusion has emerged as a promising paradigm for accurate 3D object detection. However, performance degrades substantially when deployed in target domains different from…
DuoCast: Duo-Probabilistic Diffusion for Precipitation Nowcasting
Penghui Wen, Mengwei He, Patrick Filippi +5
Accurate short-term precipitation forecasting is critical for weather-sensitive decision-making in agriculture, transportation, and disaster response. Existing deep learning approa…
Robust Depth Super-Resolution via Adaptive Diffusion Sampling
Kun Wang, Yun Zhu, Pan Zhou +1
We propose AdaDS, a generalizable framework for depth super-resolution that robustly recovers high-resolution depth maps from arbitrarily degraded low-resolution inputs. Unlike con…
RaLiFlow: Scene Flow Estimation with 4D Radar and LiDAR Point Clouds
Jingyun Fu, Zhiyu Xiang, Na Zhao
Recent multimodal fusion methods, integrating images with LiDAR point clouds, have shown promise in scene flow estimation. However, the fusion of 4D millimeter wave radar and LiDAR…
H3R: Hybrid Multi-view Correspondence for Generalizable 3D Reconstruction
Heng Jia, Linchao Zhu, Na Zhao
Despite recent advances in feed-forward 3D Gaussian Splatting, generalizable 3D reconstruction remains challenging, particularly in multi-view correspondence modeling. Existing app…