Abstract: Recently, many self-supervised pre-training methods have been proposed to improve the performance of deep neural networks (DNNs) for 3D point clouds processing. However, the common mechanism ...

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Data plays a crucial role in training learning-based methods for 3D point cloud registration. However, the real-world dataset is expensive to build, while rendering-based synthetic data suffers from ...

UniPre3D is the first unified pre-training method for 3D point clouds that effectively handles both object- and scene-level data through cross-modal Gaussian splatting. Our proposed pre-training task ...

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Abstract: Deploying LiDAR-based detectors on edge devices presents significant challenges due to limited computing power and memory. Post-training quantization (PTQ) requires only a small dataset for ...

Imagine having your AI assistant in Cursor seamlessly trigger complex GitHub workflows—fetching issue details, analyzing commit history, and creating professional pull requests—all through natural ...