9 papers · 1 filter
Parameter-Efficient Fine-Tuning for Spiking Point Cloud Models
Zihao Guo, Jihua Zhu, Yiding Sun +2
Spiking Neural Networks (SNNs) offer energy-efficient solutions for point cloud analysis on resource-constrained devices through event-driven computation. However, existing pre-tra…
GaussFusion: Towards Multimodal 3D Gaussian Pretraining
Zhixuan You, Jihua Zhu, Yiding Sun +5
3D Gaussian Splatting provides an explicit representation that jointly models geometry and appearance, serving as a scalable foundation for 3D representation learning. Existing pre…
Again-Pose: Anchor-Guided Adaptive Inter-Frame Motion Cues Propagating for High-quality Human Pose Reconstruction
Shuaikang Zhu, Yiding Sun, Yang Yang
Reconstructing continuous 3D human poses from unconstrained videos is challenging, especially in extreme motion scenarios involving severe motion blur and occlusion. Current state-…
Tri-Efficient Transfer Learning for Point Cloud Videos
Yiding Sun, Dongxu Zhang, Jihua Zhu +6
While point cloud foundation models have significantly advanced point cloud video understanding, existing parameter-efficient fine-tuning (PEFT) methods still suffer from two criti…
PointRFT: Explicit Reinforcement Fine-tuning for Point Cloud Few-shot Learning
Yankai Wang, Yiding Sun, Qirui Wang +3
Understanding spatial dynamics and semantics in point cloud is fundamental for comprehensive 3D comprehension. While reinforcement learning algorithms such as Group Relative Policy…
CMHANet: A Cross-Modal Hybrid Attention Network for Point Cloud Registration
Dongxu Zhang, Yingsen Wang, Yiding Sun +3
Robust point cloud registration is a fundamental task in 3D computer vision and geometric deep learning, essential for applications such as large-scale 3D reconstruction, augmented…