L-PCN exploits spatial locality in point cloud networks via octree partitioning into islands and intra-island hub scheduling, delivering 55-94% less feature fetching, 45-81% less computation, and 1.2-3.2x additional speedup on FPGA prototypes.
Scannet: Richly-annotated 3d reconstructions of indoor scenes,
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
PLAF introduces a 2D pixel-wise language-aligned feature extractor paired with a redundancy-reducing storage scheme that supports accurate open-vocabulary 3D scene understanding.
citing papers explorer
-
L-PCN: A Point Cloud Accelerator Exploiting Spatial Locality through Octree-based Islandization
L-PCN exploits spatial locality in point cloud networks via octree partitioning into islands and intra-island hub scheduling, delivering 55-94% less feature fetching, 45-81% less computation, and 1.2-3.2x additional speedup on FPGA prototypes.
-
PLAF: Pixel-wise Language-Aligned Feature Extraction for Efficient 3D Scene Understanding
PLAF introduces a 2D pixel-wise language-aligned feature extractor paired with a redundancy-reducing storage scheme that supports accurate open-vocabulary 3D scene understanding.