Keskeiset käsitteet
Developing efficient and effective 3D perception methods using large sparse kernels is crucial for autonomous systems.
Tiivistelmä
The content discusses the development of LSK3DNet, a Large Sparse Kernel 3D Neural Network, for efficient and effective 3D perception. It introduces Spatial-wise Dynamic Sparsity (SDS) and Channel-wise Weight Selection (CWS) components to enhance performance while reducing model size and computational cost. The method is evaluated on benchmark datasets, showcasing state-of-the-art performance compared to classical models.
Introduction
Autonomous systems require efficient LiDAR perception methods.
Point-based vs. sparse convolution methods in processing point clouds.
Large-Kernel Models
LargeKernel3D explores large 3D kernels with Spatial-wise Group Convolution.
Challenges of scaling up kernel size in 3D vision tasks.
Methodology
Formulation of the problem for 3D perception tasks.
Review of Submanifold Sparse Convolution for feature extraction.
Spatial-wise Dynamic Sparsity (SDS)
Process of Sparse Kernel Initialization and Weight Pruning/Growth.
Channel-wise Weight Selection (CWS)
Selective identification of salient channels during training.
Network Architecture
Segmentation network design on SemanticKITTI, ScanNet v2, and KITTI datasets.
Experiment Results
Performance evaluation on SemanticKITTI single-scan and multi-scan tracks.
Comparison with state-of-the-art methods on ScanNet v2 for indoor scene segmentation.
Detection performance evaluation on the car category in the KITTI dataset.
Ablation Studies
Impact of kernel size on performance and overall architecture design.
Hyperparameter choices for SDS and CWS components.
Tilastot
"LSK3DNet achieves state-of-the-art performance on SemanticKITTI."
"LSK3DNet reduces model size by roughly 40% compared to naive large 3D kernel models."
Lainaukset
"Our method achieves better performance compared to state-of-the-art methods."
"LSK3DNet outperforms the prior 3D large kernel method."