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English(EN) Memory-efficient GPU pipelines for real-time non-line-of-sight reconstruction

新的GPU流水线提高了NLOS成像重建的速度和效率

研究人员开发了新的GPU流水线,以显著提高非视线(NLOS)成像重建的效率。这些流水线通过融合内核、使用warp级光子分箱和采用FP16存储来优化已建立的基于波的算法,如f-k迁移和phasor-fields。优化后的实现实现了高达42倍的速度提升,并将内存使用量减少了高达97.5%,从而能够在现有硬件上实现更大、更详细的重建。 AI

影响 为自动导航和机器人等应用实现更复杂、更详细的实时场景重建。

排序理由 详细介绍一种特定科学成像技术的新型计算方法的论文。[lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv cs.CV 阅读 →

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新的GPU流水线提高了NLOS成像重建的速度和效率

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详细介绍一种特定科学成像技术的新型计算方法的论文。[lever_c_demoted from research: ic=1 ai=0.7]
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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Alfonso L\'opez-Ruiz, Diego Royo ·

    面向实时非视线重建的内存高效GPU管道

    arXiv:2608.28183v1 Announce Type: cross Abstract: Non-line-of-sight (NLOS) imaging reconstructs scenes hidden around a corner from indirect light recorded by a single-photon avalanche diode (SPAD). A single reconstruction is a large inverse problem: billions of photon timestamps …