PACE achieves state-of-the-art LiDAR point cloud compression with over 90% lower decoding latency by using a non-causal backbone and a stage-scalable causal predictor.
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SparseGF is a height-aware sparse segmentation framework with context compression that improves robustness of ground filtering in ALS point clouds from urban to natural scenes.
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PACE: Post-Causal Entropy Modeling for Learned LiDAR Point Cloud Compression
PACE achieves state-of-the-art LiDAR point cloud compression with over 90% lower decoding latency by using a non-causal backbone and a stage-scalable causal predictor.
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SparseGF: A Height-Aware Sparse Segmentation Framework with Context Compression for Robust Ground Filtering Across Urban to Natural Scenes
SparseGF is a height-aware sparse segmentation framework with context compression that improves robustness of ground filtering in ALS point clouds from urban to natural scenes.