2 citations · 3 across the 7 of their papers we have counts for
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Diffusion Masked Pretraining for Dynamic Point Cloud
Zhuoyue Zhang, Jihua Zhu, Yiding Sun +4
Dynamic point cloud pretraining is still dominated by masked reconstruction objectives. However, these objectives inherit two key limitations. Existing methods inject ground-truth…
Mantis: Mamba-native Tuning is Efficient for 3D Point Cloud Foundation Models
Zihao Guo, Jihua Zhu, Yiding Sun +3
Pre-trained 3D point cloud foundation models (PFMs) have demonstrated strong transferability across diverse downstream tasks. However, full fine-tuning these models is computationa…
PVContext: Hybrid Context Model for Point Cloud Compression
Guoqing Zhang, Wenbo Zhao, Jian Liu +3
Efficient storage of large-scale point cloud data has become increasingly challenging due to advancements in scanning technology. Recent deep learning techniques have revolutionize…
Diffusion-Occ: 3D Point Cloud Completion via Occupancy Diffusion
Guoqing Zhang, Jian Liu
Point clouds are crucial for capturing three-dimensional data but often suffer from incompleteness due to limitations such as resolution and occlusion. Traditional methods typicall…
DINO-SD: Champion Solution for ICRA 2024 RoboDepth Challenge
Yifan Mao, Ming Li, Jian Liu +7
Surround-view depth estimation is a crucial task aims to acquire the depth maps of the surrounding views. It has many applications in real world scenarios such as autonomous drivin…
Stealing Stable Diffusion Prior for Robust Monocular Depth Estimation
Yifan Mao, Jian Liu, Xianming Liu
Monocular depth estimation is a crucial task in computer vision. While existing methods have shown impressive results under standard conditions, they often face challenges in relia…
REPS: Reconstruction-based Point Cloud Sampling
Guoqing Zhang, Wenbo Zhao, Jian Liu +1
Sampling is widely used in various point cloud tasks as it can effectively reduce resource consumption. Recently, some methods have proposed utilizing neural networks to optimize t…