Real-Time Decompression and Rasterization of Massive Point Clouds

Abstract

Large-scale capturing of real-world scenes as 3D point clouds (e.g., using LIDAR scanning) generates billions of points that are challenging to visualize. High storage requirements prevent the quick and easy inspection of captured datasets on user-grade hardware. The fastest real-time rendering methods are limited by the available GPU memory and render only around 1 billion points interactively. We show that we can achieve state-of-the-art in both while simultaneously supporting datasets that surpass the capabilities of other methods. We present an on-the-fly point cloud decompression scheme that tightly integrates with software rasterization to reduce on-chip memory requirements by more than 4×. Our method compresses geometry losslessly and provides high visual quality at real-time framerates. We use a GPU-friendly, clipped Huffman encoding for compression. Point clouds are divided into equal-sized batches, which are Huffman-encoded independently. Batches are further subdivided to form easy-to-consume streams of data for massively parallel execution. The compressed point clouds are stored in an access-aware manner to achieve coherent GPU memory access and a high L1 cache hit rate at render time. Our approach can decompress and rasterize up to 120 million Huffman-encoded points per millisecond on-the-fly. We evaluate the quality and performance of our approach on various large datasets against the fastest competing methods. Our approach renders massive 3D point clouds at competitive frame rates and visual quality while consuming significantly less memory, thus unlocking unprecedented performance for the visualization of challenging datasets on commodity GPUs.