3DTMDet proposes a hybrid Mamba-Transformer architecture with a 3DHMT block and LiDAR-inspired voxel generation to improve 3D object detection in point clouds, outperforming prior methods on KITTI and ONCE datasets.
Lion:Lineargrouprnnfor3dobjectdetectioninpointclouds
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FALO achieves competitive accuracy on nuScenes and Waymo LiDAR benchmarks while running 1.6-9.8x faster than prior state-of-the-art methods on mobile GPUs and NPUs through a hardware-friendly voxel sequencing and ConvDotMix architecture.
citing papers explorer
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3DTMDet: A Dual-Path Synergy Network of Transformer and SSM for 3D Object Detection in Point Clouds
3DTMDet proposes a hybrid Mamba-Transformer architecture with a 3DHMT block and LiDAR-inspired voxel generation to improve 3D object detection in point clouds, outperforming prior methods on KITTI and ONCE datasets.
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FALO: Fast and Accurate LiDAR 3D Object Detection on Resource-Constrained Devices
FALO achieves competitive accuracy on nuScenes and Waymo LiDAR benchmarks while running 1.6-9.8x faster than prior state-of-the-art methods on mobile GPUs and NPUs through a hardware-friendly voxel sequencing and ConvDotMix architecture.