Real-time Light Detection And Ranging (LiDAR) simulation must find, per emitted ray, the closest intersecting triangle even in dynamic scenes containing large numbers of moving and deformable objects. Dominant acceleration-structure approaches require rebuilding each frame for dynamic geometry -- a cost that compounds directly with scene dynamics and cannot be amortized regardless of how little actually changed. This paper presents the Gajmer Ray-Casting Algorithm (GRCA), which inverts the question: instead of asking what does each ray hit? it asks which rays can each triangle possibly hit? GRCA geometrically models spinning LiDAR emitters as rotation-traced cones or planes and uses each triangle's emitter-centric apparent area to cull, per triangle, which channels and the rays within those channels can possibly reach it -- without any acceleration structure. GRCA is compute-based and vendor-agnostic by design, targeting highly dynamic, high-resolution simultaneous multi-sensor simulation. At its core, GRCA is a general-purpose ray-casting algorithm: the emitter-centric inversion applies to any setting where rays originate from a known position, not only LiDAR. Benchmarks evaluate 2-8 simultaneous 128x4096-ray LiDARs (360deg/180deg) over complex dynamic scenes -- with just two sensors casting ~1M rays per frame. With range culling inactive, GRCA reaches up to 7.97x over hardware-accelerated OptiX (GPU) and 14.55x over Embree (CPU). Two independent extensions further boost performance even in the most complex scene (~22M triangles, ~9M of which are dynamic, 8 LiDARs): range culling at realistic deployment ranges (10-100m) reaches up to 7.02x GPU and 9.33x CPU; a hybrid pipeline -- GRCA for dynamic geometry, OptiX/Embree for static -- reaches up to 10.5x GPU and 19.2x CPU.

翻译：实时激光雷达（LiDAR）仿真必须为每条发射射线找到最近的相交三角形，即使是在包含大量移动和可变形物体的动态场景中。主流加速结构方法需要为每一帧重建动态几何体——这种开销随场景动态性直接增加，且无论实际变化多小都无法摊销。本文提出Gajmer射线投射算法（GRCA），该算法反转了问题：不再询问每条射线击中什么，而是询问每个三角形可能击中哪些射线。GRCA将旋转LiDAR发射器几何建模为旋转迹线锥体或平面，并利用每个三角形以发射器为中心的表观面积，为每个三角形剔除可能到达它的通道及其内部射线——无需任何加速结构。GRCA基于计算且设计为供应商无关，针对高度动态、高分辨率的多传感器同步仿真。其核心是通用射线投射算法：以发射器为中心的反转方法适用于任何射线从已知位置发射的场景，不仅限于LiDAR。基准测试评估了2-8个同时运行的128×4096射线LiDAR（360°/180°）在复杂动态场景上的性能——仅两个传感器每帧就投射约100万条射线。在未启用距离剔除时，GRCA比硬件加速的OptiX（GPU）快达7.97倍，比Embree（CPU）快达14.55倍。两个独立扩展进一步提升了性能，即使在最复杂场景（约2200万三角形，其中约900万为动态，8个LiDAR）中：在真实部署距离（10-100米）下的距离剔除达到GPU 7.02倍和CPU 9.33倍；混合流水线——GRCA处理动态几何体，OptiX/Embree处理静态几何体——达到GPU 10.5倍和CPU 19.2倍。