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Lossy Point Cloud Geometry Compression via End to End Learning
[Sparse'25] Compiling and Compressing Structured Tensors
ASPLOS'23 - Session 4C - The Sparse Abstract Machine
Layer-Wise Geometry Aggregation Framework for Lossless LiDAR Point Cloud Compression
SPLATNet: Sparse Lattice Networks for Point Cloud Processing (CVPR '18)
Learning-based lossless compression of 3D point cloud geometry - Dat T. Nguyen
Talk by L. Wiesmann: Deep Compression for Dense Point Cloud Maps (RAL-ICRA 2021)
VGGT-Diff: Visual Geometry Meets Diffusion for Sparse-View Novel View Synthesis
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Last Updated: October 21, 2026
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Sparse Tensor Based Multiscale Representation for Point Cloud Geometry Compression Project website (w/ code): sparseloop.mit.edu/ Paper: sparseloop.mit.edu/documents/2022-micro-sparseloop.pdf ... Transmission experiment using real-time codec compliant with the latest international standard of This paper presents a novel end-to-end Learned ASPLOS'23: The 28th International Conference on Architectural Support for Programming Languages and Operating Systems ... A fast and end-to-end trainable neural network that directly works on This conference is intended mainly for first and second university, PhD and Master students and third year students in engineering ... L. Wiesmann, A. Milioto, X. Chen, C. Stachniss, and J. Behley, “Deep A little static-scene, implicit Atlas energy? Perhaps. From just six
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