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.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
PULSE stabilizes mmWave human pose estimation by screening Doppler motion prompts before injecting them into spatial magnitude reasoning.
citing papers explorer
-
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.
-
Doppler Prompting for Stable mmWave-based Human Pose Estimation
PULSE stabilizes mmWave human pose estimation by screening Doppler motion prompts before injecting them into spatial magnitude reasoning.