reading-sensor-pointclouds

CPU and CUDA memory mapping differ in synchronization requirements. CUDA mapping requires explicit synchronization and copying data before unmapping, while CPU mapping handles valid entries directly. Both need correct sensor output mapping and lifetime management for reliable point cloud processing.

Radar point cloud composite render-var tensors expose channels including Coordinates, Counts, Intensity, RCS, RadialVelocityMs, and TimeOffsetNs. Correct channel handling and sensor output mapping are required to access these measurements reliably.

When mapping point cloud tensors in CUDA memory, you must synchronize operations and copy data before unmapping to prevent loss. Proper lifetime management ensures valid entries are processed safely during sensor data workflows.

Invalid point cloud tensor entries often result from incorrect sensor output mapping, missing synchronization, or premature unmapping. Ensure correct channel handling, copy data before unmapping, and manage tensor lifetime to extract valid points reliably.

Lidar and radar point cloud outputs share the same composite render-var tensor structure but expose different channels like Intensity versus RCS. Both require correct sensor output mapping, channel handling, and valid-entry extraction for reliable data processing.