A parameter-shared residual MLP with per-point normalization reaches competitive point cloud accuracy at 0.58M parameters and runs 3.7x faster on an FPGA than the CPU baseline.
F-LOAM: Fast LiDAR Odometry and Mapping
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PointODE: Lightweight Point Cloud Learning with Neural Ordinary Differential Equations on Edge
A parameter-shared residual MLP with per-point normalization reaches competitive point cloud accuracy at 0.58M parameters and runs 3.7x faster on an FPGA than the CPU baseline.