LiDAR Snowfall Simulation for Robust 3D Object Detection
Martin Hahner, Christos Sakaridis, Mario Bijelic, Felix Heide, Fisher Yu, Dengxin Dai, Luc Van Gool

Figure 1. 3D object detection results in heavy snowfall with prior training on the proposed data augmentation scheme (top right) in comparison to no augmentation (top left). The bottom row shows the RGB image as reference.

Figure 2. Simulated snowfall corresponding to a snowfall rate of rs = 2.5 mm/h. The left block shows the clear undisturbed input. The right block shows our snowfall simulation (top) and the snowfall simulation in LISA (bottom). Note that we simulate the scattering realistically and only attenuate points which are affected by individual snowflakes instead of attenuating all points based on their distance.

Figure.3: Snow particles interfering a single LiDAR beam (top). Schematic plot of corresponding received power echoes (bottom). Note how the received power of individual targets can overlap with each other (cτH ≈ 3 m with τH = 10 ns).

Figure 4. A real-world capture on a dry highway (top), a realworld capture with a water height of dw = 0.53 mm (middle) and the synthesized road wetness from the clear reference (bottom).

Figure 5: Visualization showing the geometrical optical model which describes the reflection on a wet road surface

Table 1. Comparison of simulation methods for 3D object detection in snowfall on STF [1]. We report 3D average precision (AP) of moderate cars on three STF splits: the heavy snowfall test split with 1404 samples, the light snowfall test split with 2512 samples and the clear-weather test split with 1816 samples. “Ours-wet”: our wet ground simulation, “Ours-snow”: our snowfall simulation, “Ourssnow+wet”: cascaded application of our snowfall and wet ground simulation.

Figure 6: Qualitative comparison of PV-RCNN on samples from STF containing heavy snowfall. The leftmost column shows the corresponding RGB images. The rest of the columns show the LiDAR point clouds with ground-truth boxes and predictions using the clear-weather baseline (“no augmentation”), DROR, LISA, and our fully-fledged simulation (“our snow+wet augmentation”).