  The robustness of driving perception systems under unprecedented conditions
is crucial for safety-critical usages. Latest advancements have prompted
increasing interests towards multi-LiDAR perception. However, prevailing
driving datasets predominantly utilize single-LiDAR systems and collect data
devoid of adverse conditions, failing to capture the complexities of real-world
environments accurately. Addressing these gaps, we proposed Place3D, a
full-cycle pipeline that encompasses LiDAR placement optimization, data
generation, and downstream evaluations. Our framework makes three appealing
contributions. 1) To identify the most effective configurations for multi-LiDAR
systems, we introduce a Surrogate Metric of the Semantic Occupancy Grids
(M-SOG) to evaluate LiDAR placement quality. 2) Leveraging the M-SOG metric, we
propose a novel optimization strategy to refine multi-LiDAR placements. 3)
Centered around the theme of multi-condition multi-LiDAR perception, we collect
a 364,000-frame dataset from both clean and adverse conditions. Extensive
experiments demonstrate that LiDAR placements optimized using our approach
outperform various baselines. We showcase exceptional robustness in both 3D
object detection and LiDAR semantic segmentation tasks, under diverse adverse
weather and sensor failure conditions. Code and benchmark toolkit are publicly
available.
