Supplementary Material for
PointFlowNet: Learning Representations for
Rigid Motion Estimation from Point Clouds
Aseem Behl
Despoina Paschalidou
Simon Donné
Andreas Geiger
Autonomous Vision Group, MPI for Intelligent Systems and University of Tübingen{aseem.behl,despoina.paschalidou,simon.donne,andreas.geiger}@tue.mpg.deAbstract
The supplementary material provides additional qualitative comparisons of our method with the baselines methods
along with examples of failure cases for our method. We also
describe our workflow for generating the Augmented KITTI
dataset and provide the hyperparameter values for balancing weights in our multi-task loss function. In addition, weprovide a video of our qualitative results on sequences fromKITTI test dataset.

1. Augmented KITTI
Figure 1 describes our workflow for generating the Augmented KITTI. We start by fitting the ground plane using
RANSAC 3D plane fitting; this allows us to detect obstacles
and hence the drivable region. In a second step, we randomly
place virtual cars in the drivable region, and simulate a new
LIDAR scan that includes these virtual cars. Our simulator
uses a noise model learned from the real KITTI scanner by
fitting a Gaussian distribution conditioned on the horizontal
and vertical angle of the rays, based on KITTI LIDAR scans.
Our simulator also produces missing estimates at transparent
surfaces by ignoring them with a probability equal to theirtransparency value provided by the CAD models. Additionally, we remove points in the original scan which become
occluded by the augmented car by tracing a ray between eachpoint and the LIDAR, and removing those points whose rayintersects with the car mesh. Finally, we sample the augmented car’s rigid motion using a simple approximation of
the Ackermann steering geometry, place the car at the corresponding location in the next frame, and repeat the LIDAR
simulation.

2. Hyperparameters
The loss functions used by our approach comprises fourparts:
L = αLf low + βLrigmo + γLego + Ldet

(1)

Here, α, β, γ are positive constants for balancing the relative
importance of the task specific loss functions. In order tofix the balancing weights in our multi-task loss function, weperformed a hyperparameter search over the loss function
weights, maximizing the performance over the scene flow
and object rigid motion. We set α = 4.0, β = 1.0, γ = 1.0
in Eqn. 1.

3. Qualitative Comparison to Baseline Methods
Figures 2 - 23 show qualitative comparison of our methodwith the best performing baseline methods on examples
from the test set of the Augmented KITTI dataset. The
qualitative results show that our method predicts motion forboth background and foreground parts of the scene withhigher accuracy than all the baselines on a diverse range ofscenes and motions.
In Figures 21 - 23, we provide challenging examples
where our method fails to predict the correct scene flow. Weobserve here that in case of scenes with two or more cars invery close proximity, our method may predict wrong scene
flow for points on one car in the reference point cloud atframe t by matching them with points on the other car inclose proximity at frame t + 1. However, we note that, evenfor these failure cases our method performs better than thebaseline methods.

Sample location indrivable region

RANSAC groundplane fittingLiDAR scan at time =t

Detected the ground plane and obstacles in 3D
LiDAR data

LiDAR Scan of a real outdoor scene at t augmented
with scan & 3D bounding box of a synthetic car
Sample CAD model

Sample realistic rigid
body motion

Over 100 detailed 3D car CAD models

LiDAR scan at time =t+1

Sample CAD model

LiDAR Scan of a real outdoor scene at t+1 augmented
with scan & 3D bounding box of a synthetic car

Figure 1: Augmented KITTI. Workflow for generating the Augmented KITTI dataset.

Figure 2: Qualitative Comparison of our method with the best performing baseline methods on an example from the test setof the Augmented KITTI dataset. For clarity, we visualize only a subset of the points.

Figure 3: Qualitative Comparison of our method with the best performing baseline methods on an example from the test setof the Augmented KITTI dataset. For clarity, we visualize only a subset of the points.

Figure 4: Qualitative Comparison of our method with the best performing baseline methods on an example from the test setof the Augmented KITTI dataset. For clarity, we visualize only a subset of the points.

Figure 5: Qualitative Comparison of our method with the best performing baseline methods on an example from the test setof the Augmented KITTI dataset. For clarity, we visualize only a subset of the points.

Figure 6: Qualitative Comparison of our method with the best performing baseline methods on an example from the test setof the Augmented KITTI dataset. For clarity, we visualize only a subset of the points.

Figure 7: Qualitative Comparison of our method with the best performing baseline methods on an example from the test setof the Augmented KITTI dataset. For clarity, we visualize only a subset of the points.

Figure 8: Qualitative Comparison of our method with the best performing baseline methods on an example from the test setof the Augmented KITTI dataset. For clarity, we visualize only a subset of the points.

Figure 9: Qualitative Comparison of our method with the best performing baseline methods on an example from the test setof the Augmented KITTI dataset. For clarity, we visualize only a subset of the points.

Figure 10: Qualitative Comparison of our method with the best performing baseline methods on an example from the test
set of the Augmented KITTI dataset. For clarity, we visualize only a subset of the points.

Figure 11: Qualitative Comparison of our method with the best performing baseline methods on an example from the test
set of the Augmented KITTI dataset. For clarity, we visualize only a subset of the points.

Figure 12: Qualitative Comparison of our method with the best performing baseline methods on an example from the test
set of the Augmented KITTI dataset. For clarity, we visualize only a subset of the points.

Figure 13: Qualitative Comparison of our method with the best performing baseline methods on an example from the test
set of the Augmented KITTI dataset. For clarity, we visualize only a subset of the points.

Figure 14: Qualitative Comparison of our method with the best performing baseline methods on an example from the test
set of the Augmented KITTI dataset. For clarity, we visualize only a subset of the points.

Figure 15: Qualitative Comparison of our method with the best performing baseline methods on an example from the test
set of the Augmented KITTI dataset. For clarity, we visualize only a subset of the points.

Figure 16: Qualitative Comparison of our method with the best performing baseline methods on an example from the test
set of the Augmented KITTI dataset. For clarity, we visualize only a subset of the points.

Figure 17: Qualitative Comparison of our method with the best performing baseline methods on an example from the test
set of the Augmented KITTI dataset. For clarity, we visualize only a subset of the points.

Figure 18: Qualitative Comparison of our method with the best performing baseline methods on an example from the test
set of the Augmented KITTI dataset. For clarity, we visualize only a subset of the points.

Figure 19: Qualitative Comparison of our method with the best performing baseline methods on an example from the test
set of the Augmented KITTI dataset. For clarity, we visualize only a subset of the points.

Figure 20: Qualitative Comparison of our method with the best performing baseline methods on an example from the test
set of the Augmented KITTI dataset. For clarity, we visualize only a subset of the points.

Figure 21: Qualitative Comparison of our method with the best performing baseline methods on an example from the test
set of the Augmented KITTI dataset. We observe here that our method predicts wrong scene flow for points on the green car in
the reference point cloud at frame t by matching them with points on the other car (pink) in close proximity at frame t + 1.
For clarity, we visualize only a subset of the points.

Figure 22: Qualitative Comparison of our method with the best performing baseline methods on an example from the test
set of the Augmented KITTI dataset. We observe here that our method predicts wrong scene flow for points on the red car in
the reference point cloud at frame t by matching them with points on the other car (green) in close proximity at frame t + 1.
For clarity, we visualize only a subset of the points.

Figure 23: Qualitative Comparison of our method with the best performing baseline methods on an example from the test
set of the Augmented KITTI dataset. We observe here that our method predicts wrong scene flow for points on the green car in
the reference point cloud at frame t by matching them with points on the other car (yellow) in close proximity at frame t + 1.
For clarity, we visualize only a subset of the points.