Supplementary Material:
Object Scene Flow for Autonomous Vehicles
Moritz Menze
Leibniz Universität Hannover

Andreas Geiger
MPI Tübingenmenze@ipi.uni-hannover.de

andreas.geiger@tue.mpg.de

Abstract
This supplementary document provides additional descriptions, visualizations and experiments. We start by visualizing
the 3D CAD models used for generating our scene flow ground truth. Next, we provide a detailed description of the initialization procedure for object hypotheses in our model. For reproducibility, we also detail all (estimated) model parameters
together with plots illustrating the sensitivity of our model with respect to the choice of parameters. We further demonstrate that the loss in performance is small when limiting the runtime of our method to two minutes per scene on a single
core. We also provide additional qualitative results of our method on the sphere sequence and quantitatively compare ourmethod to state-of-the-art stereo, optical flow and scene flow approaches on the well-established KITTI stereo and opticalflow benchmarks. Finally, we show additional quantitative and qualitative results on the novel scene flow dataset.
The supplementary video contained in the zip file visualizes our scene flow estimation results on three challenging
sequences from the KITTI raw dataset by processing each frame individually.

1. 3D CAD Models for Ground Truth Annotation
Figure 1 depicts the 3D CAD models we used to generate ground truth for the proposed scene flow dataset. In orderto adjust the shape of the models to the image observations/laser scans, we estimate all 9 parameters of a 3D similarity
transformation as explained in section 4.2 of the paper.

Figure 1: 3D CAD Models. This figure shows the 3D CAD models we used to create ground truth for the proposed dataset.

12. Initialization of Object Hypotheses
In this section we provide a more detailed description of the procedure we use to establish initial object hypotheses. We
start by extracting sparse scene flow correspondences and robustly estimating the dominant motion in the scene (i.e., thebackground/camera motion) using the approach described in [3]. In order to reduce the number of outliers in the correspondence set, we mask all (background) feature points in regions which leave the image domain according to the estimateddominant motion and the initial SGM disparity estimate as illustrated in Fig. 2.

Figure 2: Visibility Mask. Image regions visible in the first frame but not in the second frame are shown in white.

Figure 3: Object Hypotheses Generation. This figure depicts the optical flow field induced by the camera motion and theremaining sparse scene flow features grouped into individual motion hypotheses using color-coded flow vectors.
Given the scene flow correspondences which do not agree with the background motion (deviate by more than 5 pixels), we
extract object hypotheses as follows: We randomly sample 50 correspondences and fit a rigid motion model to all correspondences within a ball of radius 2.5 meters in 3D using the 3-point RANSAC algorithm. The resulting proposals are sorted withrespect to the number of respective inliers. Furthermore, we apply non-maxima suppression to avoid multiple overlapping
proposals on the same object. The process is illustrated in Fig. 3, where 3 motion hypotheses have been recovered.

2

3. Training of Parameters
In order to obtain the model parameters {θ} and {τ }, we perform block coordinate descent on a subset of 30 randomly
selected training images. The learned weights and truncation thresholds are shown in Table 1. Following the notation in thepaper, we denote the truncation thresholds for sparse features by τ1,x where x ∈ {stereo, flow, cross}. The truncation valueof all dense Census features is denoted Cmax . The provided values refer to the normalized Hamming distance. In contrast to
the description in the paper the outlier penalty C of the dense Census features does not equal the truncation value Cmax but isestimated from training data according to Table 1. We will correct this in the final version.

Scene FlowKITTI Stereo/Flowθ1,stereo
1.0
1.0

Scene FlowKITTI Stereo/Flowθ1,flow
1.0
1.0

Cmax
0.83610.9096

θ1,cross
1.0
1.0
C
0.35900.0546

θ2,stereo
0.0235
1.4916
τ1,stereo
2.42782.5252

θ2,flow
0.5791
1.8727
τ1,flow
3.39479.5897

θ2,cross
0.5791
1.8727
τ1,cross
3.39479.5897θ30.37501.2000τ22.55595.0700θ4
17.3949538.6560
τ30.25130.1844

θ5
166.2633
0.2633λ0.13240.0001

Table 1: Trained parameters. This table shows the values of the model parameters after training.

4. Sensitivity to Choice of Parameters
In this section we provide plots illustrating the sensitivity of our model to the choice of parameters. For each weight inTable 1 we investigate a range from 0.0 to 20 times the estimated optimal value. The first row of Fig. 4 shows that the weightof the dense census features has a significant impact on the performance while the weights of the sparse features are notthat critical. Regarding the smoothness terms depicted in the second row, the weight of the boundary term has a dominant
influence while the sensitivity with respect to the weights of normal and motion smoothness terms is relatively low. These
results are in accordance with the results of the ablation studies described in Section 5 of the paper submission.

16
14
12

16
14

5

θ

10

15

0

0.1175 0.235 0.3525 0.47

15

25

25

20

20

15

SF bg+fg occ
trained value

SF bg+fg occ
trained value

10

3.75

θ

3

5.625

7.5

0

86.9745 173.949 260.9235 347.898

θ42.8955θ

5.791

8.6865 11.582

2,flow,crossError (%)

Error (%)

20

1.875

0

θ

2,stereo

25

0

14

10

20

1,{stereo,flow,cross}

10

16

12

10
0

SF bg+fg occ
trained value

18

12

10

Error (%)

20

SF bg+fg occ
trained value

18

Error (%)

18

Error (%)

20

SF bg+fg occ
trained value

Error (%)

20

15
SF bg+fg occ
trained value

100831.32 1662.63 2493.95 3325.27

θ5Figure 4: Sensitivity with respect to Choice of Parameters. The first row shows the impact of the data term weights. The
second row shows the impact of smoothness term weights.

3

5. Results on the Proposed Dataset for Non-Occluded Regions
Table 2 provides additional quantitative results of our method and the baselines on the proposed dataset when excluding
all half-occluded regions in the image (i.e., we do not evaluate pixels leaving the image domain in any of the four views).
The proposed approach significantly outperforms all baselines with respect to scene flow estimation performance. This is thecorrected version of the table with slightly adapted occlusion masks.
D1bg
fg bg+fg bg
Huguet [6]26.38 19.88 25.31 52.30
GCSF [2]11.24 26.26 13.72 21.88
SGM [4] + LDOF [1]4.75 14.22 6.31 17.08
SGM [4] + Sun [7]4.75 14.22 6.31 15.72
SGM [4] + Sphere Flow [5]4.75 14.22 6.31 8.34
PRSF [9]4.41 13.09 5.84 6.35
Unary (SGM+SpF)
4.50 12.99 5.90 5.02
Unary (Census)
6.06 18.24 8.07 6.40
Unary (All)5.23 15.65 6.95 5.62
Unary (All) + Pair (Boundary) 4.17 9.87 5.11 4.58
Unary (All) + Pair (Normal)
4.98 15.10 6.65 5.38
Unary (All) + Pair (Object)
5.37 16.80 7.26 5.71
Unary (SGM+SpF) + Pair (All) 6.19 24.82 9.26 6.48
Unary (Census) + Pair (All)4.26 11.75 5.50 4.57
Unary (All) + Pair (All) Fast
4.01 11.76 5.29 4.38
Unary (All) + Pair (All)4.14 11.12 5.29 4.49

D2
fg bg+fg bg
40.83 50.24 41.15
31.66 23.63 38.12
18.66 17.36 30.41
20.79 16.63 23.04
18.71 10.20 13.36
16.12 8.10 6.94
23.64 8.35 5.70
21.37 9.08 5.63
19.83 8.17 5.15
16.13 6.65 4.41
19.59 7.93 4.97
20.22 8.31 5.15
30.84 10.84 7.00
16.96 6.79 4.25
17.07 6.65 4.20
16.33 6.61 4.21

Fl
fg bg+fg bg
44.15 41.70 61.14
41.53 38.74 43.64
31.34 30.57 33.00
41.92 26.47 26.22
25.21 15.51 15.28
23.64 9.97 8.35
32.98 10.65 7.41
23.02 8.78 7.51
22.30 8.26 6.77
20.28 7.29 5.73
22.05 8.07 6.51
21.26 8.07 6.79
34.34 11.96 8.96
19.09 6.94 5.59
20.02 7.07 5.53
18.65 6.83 5.52SF
fg bg+fg
60.38 61.00
55.02 45.68
39.44 34.15
48.61 30.23
32.33 18.33
28.45 11.95
37.22 12.75
31.03 11.72
29.45 10.83
25.74 9.31
29.14 10.57
28.76 10.73
40.34 14.58
25.43 9.14
26.22 9.24
24.58 8.93

Table 2: Quantitative Results on the Proposed Scene Flow Dataset for all Non-Occluded Regions. This table shows thedisparity (D1/D2), flow (Fl) and scene flow (SF) errors averaged over all 200 test images. For each modality we separately
provide the errors for the background region (bg), all foreground objects (fg) as well as all pixels in the image (bg+fg).

4

6. Sphere Sequence
We also provide results of our method on the synthetic “Sphere” dataset by Huguet et al. [6]. As described in the paper,
we modify the StereoSLIC algorithm to consider dense optical flow instead of disparity for superpixel estimation and provide
the Horn-Schunck results of Sun et al. [7] as input. Fig. 5 shows our results in terms of disparity, optical flow and color-coded
error maps. Our method performs surprisingly well despite the fact that we restrict the scene to only 200 planar superpixels.

Input

Disparity 1

Error D1

Segmentation

Disparity 2

Error D2

Superpixels

Optical Flow

Error FlRMSE 2D Flow
RMSE Disparity
RMSE Scene Flow

[8]
0.63
3.8
1.76

[6]
0.69
3.8
2.51

[10]
0.77
10.9
2.55

[9]
0.63
2.84
1.73

Ours
0.55
2.58
0.75

Figure 5: Illustration of our results for the synthetic “Sphere” sequence [6]. This figure shows (top-left to bottom-right):
The left input image of the first frame, our first disparity/error map, the obtained segmentation into different rigid body
motions, the second disparity/error map, the superpixels we use, the recovered optical flow/error map.

5

7. KITTI Benchmark
We also compare our results with respect to the state-of-the-art on the popular KITTI stereo and optical flow benchmarks1 .
Despite the fact that this dataset only contains static scenes our method performs excellent, yielding rank 5 in the stereo
benchmark (Table 3) and rank 4 in the optical flow evaluation (Table 4) including anonymous submissions, motion stereo
methods and approaches that make use of more than four images. When excluding all submissions in review and methods
that make use of additional information or constraints, our method ranks first in both benchmarks. Table 3 and Table 4 show
the 50 leading entries out of 65 listed stereo and 55 listed optical flow approaches at the time of submission. The third column“Setting” specifies additional information/constraints used by the individual methods:• fl - Flow: Method uses optical flow (2 temporally adjacent images)• mv - Multiview: Method uses more than 2 temporally adjacent images• ms - Motion stereo: Method uses epipolar geometry for computing optical flow
Fig. 6 shows qualitative results for one test image as provided on the benchmark homepage.

Figure 6: Results on KITTI Stereo Benchmark. This figure shows two subsequent input images (top), the disparity andflow maps estimated by our method (center), as well as the corresponding error maps (bottom, errors ≥ 5 pixels in white).

1 http://www.cvlibs.net/datasets/kitti

6

Rank
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50

MethodCVPR 1186
MC-CNNSPS-StFl
VC-SF
Our method
CoR
SPS-St
PCBP-SS
DDS-SS
StereoSLIC
PR-Sf+E
PCBP
PR-Sceneflow
CoR-Conf
OF-MPV
AARBM
rdSGM
wSGM
ATGV
rSGM
iSGM
RBM
DLP
ALTGV
OCV-SGBM2
SNCC
AABM
GCA
Ensemble
SGM
TGV2ADCADCSGM
mSGM-LDE
CD
Toast2ITGV
RWR
RWR+GradientLDE
BSSM
OCV-SGBM
SR-TMP
MSMW
TV-WL1+ELAS
ELAS
linBP
ELSE
S+GF (Cen)SM GPTM
LAMC-DS

Setting

fl ms
fl mv
fl

fl
fl

st

mv
st
mv

Out-Noc
2.47 %
2.61 %
2.83 %
3.05 %
3.28 %
3.30 %
3.39 %
3.40 %
3.83 %
3.92 %
4.02 %
4.04 %
4.36 %
4.49 %
4.77 %
4.86 %
4.91 %
4.97 %
5.02 %
5.03 %
5.11 %
5.18 %
5.32 %
5.36 %
5.38 %
5.40 %
5.42 %
5.45 %
5.48 %
5.76 %
5.87 %
5.94 %
6.01 %
6.15 %
6.16 %
6.20 %
6.25 %
6.40 %
6.73 %
7.39 %
7.64 %
8.00 %
8.01 %
8.11 %
8.24 %
8.56 %
8.72 %
9.03 %
9.79 %
9.82 %

Out-All
3.27 %
3.84 %
3.64 %
3.31 %
4.07 %
4.10 %
4.41 %
4.72 %
4.59 %
5.11 %
4.87 %
5.37 %
5.22 %
5.26 %
5.88 %
5.94 %
6.07 %
6.18 %
6.88 %
6.60 %
7.15 %
6.21 %
7.22 %
6.49 %
6.50 %
6.44 %
6.52 %
6.54 %
7.00 %
7.00 %
6.79 %
7.68 %
8.22 %
7.47 %
7.42 %
7.30 %
7.41 %
7.49 %
8.85 %
8.79 %
9.13 %
8.65 %
9.24 %
8.43 %
9.96 %
10.70 %
9.90 %
11.21 %
11.38 %
11.49 %

Avg-Noc
0.7 px0.8 px0.8 px0.8 px0.8 px0.8 px0.9 px0.8 px0.9 px0.9 px0.9 px0.9 px0.9 px
1.0 px
1.0 px
1.0 px
1.2 px
1.3 px
1.0 px
1.1 px
1.2 px
1.1 px
1.2 px
1.1 px
1.0 px
1.2 px
1.1 px
1.1 px
1.6 px
1.2 px
1.1 px
1.4 px
1.4 px
1.3 px
1.2 px
1.3 px
1.2 px
1.4 px
1.8 px
1.4 px
1.8 px
1.9 px
1.6 px
1.5 px
1.4 px
1.7 px
1.7 px
2.1 px
2.1 px
2.1 px

Avg-All
0.9 px
1.0 px0.9 px0.8 px0.9 px0.9 px
1.0 px
1.0 px
1.0 px
1.0 px
1.0 px
1.1 px
1.1 px
1.2 px
1.2 px
1.2 px
1.3 px
1.6 px
1.6 px
1.5 px
2.1 px
1.3 px
2.0 px
1.2 px
1.2 px
1.3 px
1.3 px
1.2 px
2.1 px
1.3 px
1.2 px
1.9 px
2.4 px
1.4 px
1.4 px
1.5 px
1.4 px
1.6 px
2.5 px
1.6 px
2.0 px
2.1 px
1.7 px
1.6 px
1.6 px
2.7 px
1.9 px
3.4 px
2.6 px
2.7 px

Runtime
265 s
100 s
35 s
300 s
50 min
6s
2s
5 min
1 min
2.3 s
200 s
5 min
150 sec6s
300 s
0.25 s
10 s
6s
6 min
0.2 s
8s
0.2 s
60 s
20 s
2s
0.11 s0.43 s
10 s
135 s
3.7 s
8s
0.9 s
55 s
5s
0.03 s
7s
1 min
5s
14 s
20.7 s
1.1 s
50 s
3 min
1 min
0.3 s
1.6 min
1s
140 s
6.5 s
10.8 minTable 3: Results on the KITTI stereo benchmark. This table shows errors with respect to the (default) 3 px threshold.
7

Rank
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50

MethodVC-SF
SPS-StFl
SPS-Fl
Our method
PR-Sf+E
PCBP-Flow
PR-Sceneflow
MotionSLIC
PPR-Flow
NLTGV-SC
DDS-DFTGV2ADCSIFT
AnyFlow
BTF-ILLUM
CRT-TGV
Data-Flow
DeepFlow
EpicFlow
TVL1-HOG
MLDP-OFDescFlow
SparseFlow
CRTflowC++
TF+OFM
C+NL
NNF-Local
fSGM
TGV2CENSUS
C+NL-fast
EPPM
CVPR-738b
HS
CVPR-738a
GC-BM-Bino
IQFlow
C+NL-MeFolki
GC-BM-Mono
RSRS-Flow
ALD
LDOF
2Bit-BM-tele
HMM
DB-TV-L1GCSF
HAOF
BERLOF
RLOF
SpaGloM

Setting
st mv
st ms
ms
st
st
ms
st
ms

mv

st ms

ms

st

Out-Noc
2.72 %
2.82 %
3.38 %
3.47 %
3.57 %
3.64 %
3.76 %
3.91 %
5.76 %
5.93 %
6.03 %
6.20 %
6.37 %
6.52 %
6.71 %
7.11 %
7.22 %
7.88 %
7.91 %
8.67 %
8.76 %
9.09 %
9.43 %
10.04 %
10.22 %
10.49 %
10.68 %
10.74 %
11.03 %
12.36 %
12.75 %
13.01 %
14.75 %
16.04 %
18.83 %
18.84 %
19.19 %
19.31 %
19.38 %
20.78 %
21.37 %
21.93 %
24.10 %
24.78 %
30.87 %
33.17 %
35.87 %
37.66 %
38.60 %
41.91 %

Out-All
4.84 %
5.61 %
10.06 %
6.34 %
7.07 %
8.28 %
7.39 %
10.56 %
10.57 %
11.96 %
13.08 %
15.15 %
15.80 %
11.03 %
12.09 %
14.57 %
17.79 %
17.08 %
18.90 %
18.78 %
19.45 %
19.32 %
18.72 %
20.26 %
18.46 %
20.64 %
21.09 %
22.66 %
18.37 %
22.28 %
23.55 %
20.67 %
24.11 %
24.60 %
29.30 %
28.25 %
26.36 %
28.79 %
29.80 %
29.75 %
30.71 %
31.39 %
33.59 %
34.19 %
39.25 %
41.71 %
43.46 %
45.27 %
46.13 %
48.59 %

Avg-Noc
0.8 px0.8 px0.9 px
1.0 px0.9 px0.9 px
1.2 px0.9 px
1.3 px
1.6 px
1.6 px
1.5 px
1.5 px
1.5 px
2.0 px
1.9 px
1.5 px
1.5 px
2.0 px
2.4 px
2.1 px
2.6 px
2.7 px
2.6 px
2.0 px
2.8 px
2.7 px
3.2 px
2.9 px
3.2 px
2.5 px
2.8 px
4.0 px
3.0 px
5.0 px
3.6 px
7.4 px
5.2 px
5.0 px6.2 px10.9 px
5.6 px7.1 px
7.2 px
7.9 px
7.0 px11.1 px
8.5 px8.7 px8.6 px

Avg-All
1.3 px
1.3 px
2.9 px
1.5 px
1.6 px
2.2 px
2.8 px
2.7 px
2.9 px
3.8 px
4.2 px
4.5 px
4.3 px
2.8 px
3.9 px
5.5 px
5.8 px
3.8 px6.1 px
6.7 px
5.7 px
7.6 px
6.5 px7.1 px
5.0 px
7.2 px
7.4 px12.2 px
6.6 px
7.9 px9.2 px
6.6 px9.0 px
6.5 px12.1 px
8.8 px14.5 px10.9 px12.1 px12.1 px
16.0 px12.4 px15.2 px15.1 px14.6 px15.3 px18.3 px16.2 px16.5 px15.3 px

Runtime
300 s
35 s
11 s
50 min200 s
3 min
150 sec
11 s
800 s
16 s
1 min
12s
15 s
80 seconds
10.5 min
3 min
17 s
15 s
180 s
160 s
9.0 s
10 s
18 s
8.5 min
350 s
14.8 min1073 s60 s
4s
2.9 min0.25 s
3.6 s
2.6 min
0.28 s
1.3 s
60 s
5 min
0.026 s
1.3 s
4 min
110 s
1 min
6 min
10 min16 s
2.4 s
16.2 s0.231 s0.488 s
50 s

Table 4: Results on the KITTI optical flow benchmark. This table shows errors with respect to the (default) 3 px threshold.
88. Qualitative Results on the Proposed Dataset
In this section, we provide additional qualitative results of our method on the proposed dataset using 44 randomly selectedscenes. For each scene, we show the ground truth disparity and optical flow map overlayed with the input images, theestimated disparity and optical flow maps and the corresponding error images using a logarithmic color coding where red
shades represent errors above 3 pixels and blue shades denote errors below 3 pixels.

0.00 - 0.19

0.19 - 0.38

0.38 - 0.75

0.75 - 1.50

1.50 - 3.00

3.00 - 6.00

6.00 - 12.00 12.00 - 24.00 24.00 - 48.00

48.00 - Inf

Figure 7: Qualitiative Results. Each subfigure shows from top-to-bottom: The disparity and optical flow ground truth in thereference view, the disparity map (D1) and optical flow map (Fl) estimated by our scene flow algorithm, and the respective
error images using the color scheme depicted in the legend.

90.00 - 0.19

0.19 - 0.38

0.38 - 0.75

0.75 - 1.50

1.50 - 3.00

3.00 - 6.00

6.00 - 12.00 12.00 - 24.00 24.00 - 48.00

48.00 - Inf

Figure 8: Qualitiative Results. Each subfigure shows from top-to-bottom: The disparity and optical flow ground truth in thereference view, the disparity map (D1) and optical flow map (Fl) estimated by our scene flow algorithm, and the respective
error images using the color scheme depicted in the legend.

100.00 - 0.19

0.19 - 0.38

0.38 - 0.75

0.75 - 1.50

1.50 - 3.00

3.00 - 6.00

6.00 - 12.00 12.00 - 24.00 24.00 - 48.00

48.00 - Inf

Figure 9: Qualitiative Results. Each subfigure shows from top-to-bottom: The disparity and optical flow ground truth in thereference view, the disparity map (D1) and optical flow map (Fl) estimated by our scene flow algorithm, and the respective
error images using the color scheme depicted in the legend.

11

0.00 - 0.19

0.19 - 0.38

0.38 - 0.75

0.75 - 1.50

1.50 - 3.00

3.00 - 6.00

6.00 - 12.00 12.00 - 24.00 24.00 - 48.00

48.00 - Inf

Figure 10: Qualitiative Results. Each subfigure shows from top-to-bottom: The disparity and optical flow ground truth in
the reference view, the disparity map (D1) and optical flow map (Fl) estimated by our scene flow algorithm, and the respective
error images using the color scheme depicted in the legend.

120.00 - 0.19

0.19 - 0.38

0.38 - 0.75

0.75 - 1.50

1.50 - 3.00

3.00 - 6.00

6.00 - 12.00 12.00 - 24.00 24.00 - 48.00

48.00 - Inf

Figure 11: Qualitiative Results. Each subfigure shows from top-to-bottom: The disparity and optical flow ground truth in
the reference view, the disparity map (D1) and optical flow map (Fl) estimated by our scene flow algorithm, and the respective
error images using the color scheme depicted in the legend.

13

0.00 - 0.19

0.19 - 0.38

0.38 - 0.75

0.75 - 1.50

1.50 - 3.00

3.00 - 6.00

6.00 - 12.00 12.00 - 24.00 24.00 - 48.00

48.00 - Inf

Figure 12: Qualitiative Results. Each subfigure shows from top-to-bottom: The disparity and optical flow ground truth in
the reference view, the disparity map (D1) and optical flow map (Fl) estimated by our scene flow algorithm, and the respective
error images using the color scheme depicted in the legend.

140.00 - 0.19

0.19 - 0.38

0.38 - 0.75

0.75 - 1.50

1.50 - 3.00

3.00 - 6.00

6.00 - 12.00 12.00 - 24.00 24.00 - 48.00

48.00 - Inf

Figure 13: Qualitiative Results. Each subfigure shows from top-to-bottom: The disparity and optical flow ground truth in
the reference view, the disparity map (D1) and optical flow map (Fl) estimated by our scene flow algorithm, and the respective
error images using the color scheme depicted in the legend.

15

0.00 - 0.19

0.19 - 0.38

0.38 - 0.75

0.75 - 1.50

1.50 - 3.00

3.00 - 6.00

6.00 - 12.00 12.00 - 24.00 24.00 - 48.00

48.00 - Inf

Figure 14: Qualitiative Results. Each subfigure shows from top-to-bottom: The disparity and optical flow ground truth in
the reference view, the disparity map (D1) and optical flow map (Fl) estimated by our scene flow algorithm, and the respective
error images using the color scheme depicted in the legend.

16

0.00 - 0.19

0.19 - 0.38

0.38 - 0.75

0.75 - 1.50

1.50 - 3.00

3.00 - 6.00

6.00 - 12.00 12.00 - 24.00 24.00 - 48.00

48.00 - Inf

Figure 15: Qualitiative Results. Each subfigure shows from top-to-bottom: The disparity and optical flow ground truth in
the reference view, the disparity map (D1) and optical flow map (Fl) estimated by our scene flow algorithm, and the respective
error images using the color scheme depicted in the legend.

17

0.00 - 0.19

0.19 - 0.38

0.38 - 0.75

0.75 - 1.50

1.50 - 3.00

3.00 - 6.00

6.00 - 12.00 12.00 - 24.00 24.00 - 48.00

48.00 - Inf

Figure 16: Qualitiative Results. Each subfigure shows from top-to-bottom: The disparity and optical flow ground truth in
the reference view, the disparity map (D1) and optical flow map (Fl) estimated by our scene flow algorithm, and the respective
error images using the color scheme depicted in the legend.

18

0.00 - 0.19

0.19 - 0.38

0.38 - 0.75

0.75 - 1.50

1.50 - 3.00

3.00 - 6.00

6.00 - 12.00 12.00 - 24.00 24.00 - 48.00

48.00 - Inf

Figure 17: Qualitiative Results. Each subfigure shows from top-to-bottom: The disparity and optical flow ground truth in
the reference view, the disparity map (D1) and optical flow map (Fl) estimated by our scene flow algorithm, and the respective
error images using the color scheme depicted in the legend.

19

0.00 - 0.19

0.19 - 0.38

0.38 - 0.75

0.75 - 1.50

1.50 - 3.00

3.00 - 6.00

6.00 - 12.00 12.00 - 24.00 24.00 - 48.00

48.00 - Inf

Figure 18: Qualitiative Results. Each subfigure shows from top-to-bottom: The disparity and optical flow ground truth in
the reference view, the disparity map (D1) and optical flow map (Fl) estimated by our scene flow algorithm, and the respective
error images using the color scheme depicted in the legend.

20

0.00 - 0.19

0.19 - 0.38

0.38 - 0.75

0.75 - 1.50

1.50 - 3.00

3.00 - 6.00

6.00 - 12.00 12.00 - 24.00 24.00 - 48.00

48.00 - Inf

Figure 19: Qualitiative Results. Each subfigure shows from top-to-bottom: The disparity and optical flow ground truth in
the reference view, the disparity map (D1) and optical flow map (Fl) estimated by our scene flow algorithm, and the respective
error images using the color scheme depicted in the legend.

21

0.00 - 0.19

0.19 - 0.38

0.38 - 0.75

0.75 - 1.50

1.50 - 3.00

3.00 - 6.00

6.00 - 12.00 12.00 - 24.00 24.00 - 48.00

48.00 - Inf

Figure 20: Qualitiative Results. Each subfigure shows from top-to-bottom: The disparity and optical flow ground truth in
the reference view, the disparity map (D1) and optical flow map (Fl) estimated by our scene flow algorithm, and the respective
error images using the color scheme depicted in the legend.

22

0.00 - 0.19

0.19 - 0.38

0.38 - 0.75

0.75 - 1.50

1.50 - 3.00

3.00 - 6.00

6.00 - 12.00 12.00 - 24.00 24.00 - 48.00

48.00 - Inf

Figure 21: Qualitiative Results. Each subfigure shows from top-to-bottom: The disparity and optical flow ground truth in
the reference view, the disparity map (D1) and optical flow map (Fl) estimated by our scene flow algorithm, and the respective
error images using the color scheme depicted in the legend.

23

0.00 - 0.19

0.19 - 0.38

0.38 - 0.75

0.75 - 1.50

1.50 - 3.00

3.00 - 6.00

6.00 - 12.00 12.00 - 24.00 24.00 - 48.00

48.00 - Inf

Figure 22: Qualitiative Results. Each subfigure shows from top-to-bottom: The disparity and optical flow ground truth in
the reference view, the disparity map (D1) and optical flow map (Fl) estimated by our scene flow algorithm, and the respective
error images using the color scheme depicted in the legend.

24

0.00 - 0.19

0.19 - 0.38

0.38 - 0.75

0.75 - 1.50

1.50 - 3.00

3.00 - 6.00

6.00 - 12.00 12.00 - 24.00 24.00 - 48.00

48.00 - Inf

Figure 23: Qualitiative Results. Each subfigure shows from top-to-bottom: The disparity and optical flow ground truth in
the reference view, the disparity map (D1) and optical flow map (Fl) estimated by our scene flow algorithm, and the respective
error images using the color scheme depicted in the legend.

25

0.00 - 0.19

0.19 - 0.38

0.38 - 0.75

0.75 - 1.50

1.50 - 3.00

3.00 - 6.00

6.00 - 12.00 12.00 - 24.00 24.00 - 48.00

48.00 - Inf

Figure 24: Qualitiative Results. Each subfigure shows from top-to-bottom: The disparity and optical flow ground truth in
the reference view, the disparity map (D1) and optical flow map (Fl) estimated by our scene flow algorithm, and the respective
error images using the color scheme depicted in the legend.

26

0.00 - 0.19

0.19 - 0.38

0.38 - 0.75

0.75 - 1.50

1.50 - 3.00

3.00 - 6.00

6.00 - 12.00 12.00 - 24.00 24.00 - 48.00

48.00 - Inf

Figure 25: Qualitiative Results. Each subfigure shows from top-to-bottom: The disparity and optical flow ground truth in
the reference view, the disparity map (D1) and optical flow map (Fl) estimated by our scene flow algorithm, and the respective
error images using the color scheme depicted in the legend.

27

0.00 - 0.19

0.19 - 0.38

0.38 - 0.75

0.75 - 1.50

1.50 - 3.00

3.00 - 6.00

6.00 - 12.00 12.00 - 24.00 24.00 - 48.00

48.00 - Inf

Figure 26: Qualitiative Results. Each subfigure shows from top-to-bottom: The disparity and optical flow ground truth in
the reference view, the disparity map (D1) and optical flow map (Fl) estimated by our scene flow algorithm, and the respective
error images using the color scheme depicted in the legend.

28

0.00 - 0.19

0.19 - 0.38

0.38 - 0.75

0.75 - 1.50

1.50 - 3.00

3.00 - 6.00

6.00 - 12.00 12.00 - 24.00 24.00 - 48.00

48.00 - Inf

Figure 27: Qualitiative Results. Each subfigure shows from top-to-bottom: The disparity and optical flow ground truth in
the reference view, the disparity map (D1) and optical flow map (Fl) estimated by our scene flow algorithm, and the respective
error images using the color scheme depicted in the legend.

29

0.00 - 0.19

0.19 - 0.38

0.38 - 0.75

0.75 - 1.50

1.50 - 3.00

3.00 - 6.00

6.00 - 12.00 12.00 - 24.00 24.00 - 48.00

48.00 - Inf

Figure 28: Qualitiative Results. Each subfigure shows from top-to-bottom: The disparity and optical flow ground truth in
the reference view, the disparity map (D1) and optical flow map (Fl) estimated by our scene flow algorithm, and the respective
error images using the color scheme depicted in the legend.

30

9. Qualitative Comparison to State-of-the-Art Methods
Finally, we show a qualitative comparison of our results to those of the state-of-the-art methods [6], [2], [1], [5], and [9].
For each scene, we show the ground truth disparity and optical flow map overlayed with the input images, the estimateddisparity and optical flow maps and the corresponding error images using a logarithmic color coding where red shades
represent errors above 3 pixels and blue shades denote errors below 3 pixels. The percentage error, which is also included inour evaluation metric, is mapped so that inlier pixels with an error below 3 pixels or 5% are shown in blue shades.

0.00 - 0.19(a) Huguet [6](b) GCSF [2](c) SGM [4] + LDOF [1](d) SGM [4] + Sphere Flow [5](e) PRSF [9](f) Our full method0.19 - 0.38

0.38 - 0.75

0.75 - 1.50

1.50 - 3.00

3.00 - 6.00

6.00 - 12.00 12.00 - 24.00 24.00 - 48.00

48.00 - Inf

Figure 29: Comparison of Results. Each subfigure shows from top-to-bottom: The disparity and optical flow ground truth in
the reference view, the disparity map (D1) and optical flow map (Fl) estimated by the specified algorithm, and the respective
error images using the color scheme depicted in the legend.

31

0.00 - 0.19(a) Huguet [6](b) GCSF [2](c) SGM [4] + LDOF [1](d) SGM [4] + Sphere Flow [5](e) PRSF [9](f) Our full method0.19 - 0.38

0.38 - 0.75

0.75 - 1.50

1.50 - 3.00

3.00 - 6.00

6.00 - 12.00 12.00 - 24.00 24.00 - 48.00

48.00 - Inf

Figure 30: Comparison of Results. Each subfigure shows from top-to-bottom: The disparity and optical flow ground truth in
the reference view, the disparity map (D1) and optical flow map (Fl) estimated by the specified algorithm, and the respective
error images using the color scheme depicted in the legend.

32

0.00 - 0.19(a) Huguet [6](b) GCSF [2](c) SGM [4] + LDOF [1](d) SGM [4] + Sphere Flow [5](e) PRSF [9](f) Our full method0.19 - 0.38

0.38 - 0.75

0.75 - 1.50

1.50 - 3.00

3.00 - 6.00

6.00 - 12.00 12.00 - 24.00 24.00 - 48.00

48.00 - Inf

Figure 31: Comparison of Results. Each subfigure shows from top-to-bottom: The disparity and optical flow ground truth in
the reference view, the disparity map (D1) and optical flow map (Fl) estimated by the specified algorithm, and the respective
error images using the color scheme depicted in the legend.

33

0.00 - 0.19(a) Huguet [6](b) GCSF [2](c) SGM [4] + LDOF [1](d) SGM [4] + Sphere Flow [5](e) PRSF [9](f) Our full method0.19 - 0.38

0.38 - 0.75

0.75 - 1.50

1.50 - 3.00

3.00 - 6.00

6.00 - 12.00 12.00 - 24.00 24.00 - 48.00

48.00 - Inf

Figure 32: Comparison of Results. Each subfigure shows from top-to-bottom: The disparity and optical flow ground truth in
the reference view, the disparity map (D1) and optical flow map (Fl) estimated by the specified algorithm, and the respective
error images using the color scheme depicted in the legend.

34

0.00 - 0.19(a) Huguet [6](b) GCSF [2](c) SGM [4] + LDOF [1](d) SGM [4] + Sphere Flow [5](e) PRSF [9](f) Our full method0.19 - 0.38

0.38 - 0.75

0.75 - 1.50

1.50 - 3.00

3.00 - 6.00

6.00 - 12.00 12.00 - 24.00 24.00 - 48.00

48.00 - Inf

Figure 33: Comparison of Results. Each subfigure shows from top-to-bottom: The disparity and optical flow ground truth in
the reference view, the disparity map (D1) and optical flow map (Fl) estimated by the specified algorithm, and the respective
error images using the color scheme depicted in the legend.

35

0.00 - 0.19(a) Huguet [6](b) GCSF [2](c) SGM [4] + LDOF [1](d) SGM [4] + Sphere Flow [5](e) PRSF [9](f) Our full method0.19 - 0.38

0.38 - 0.75

0.75 - 1.50

1.50 - 3.00

3.00 - 6.00

6.00 - 12.00 12.00 - 24.00 24.00 - 48.00

48.00 - Inf

Figure 34: Comparison of Results. Each subfigure shows from top-to-bottom: The disparity and optical flow ground truth in
the reference view, the disparity map (D1) and optical flow map (Fl) estimated by the specified algorithm, and the respective
error images using the color scheme depicted in the legend.

36

0.00 - 0.19(a) Huguet [6](b) GCSF [2](c) SGM [4] + LDOF [1](d) SGM [4] + Sphere Flow [5](e) PRSF [9](f) Our full method0.19 - 0.38

0.38 - 0.75

0.75 - 1.50

1.50 - 3.00

3.00 - 6.00

6.00 - 12.00 12.00 - 24.00 24.00 - 48.00

48.00 - Inf

Figure 35: Comparison of Results. Each subfigure shows from top-to-bottom: The disparity and optical flow ground truth in
the reference view, the disparity map (D1) and optical flow map (Fl) estimated by the specified algorithm, and the respective
error images using the color scheme depicted in the legend.

37

0.00 - 0.19(a) Huguet [6](b) GCSF [2](c) SGM [4] + LDOF [1](d) SGM [4] + Sphere Flow [5](e) PRSF [9](f) Our full method0.19 - 0.38

0.38 - 0.75

0.75 - 1.50

1.50 - 3.00

3.00 - 6.00

6.00 - 12.00 12.00 - 24.00 24.00 - 48.00

48.00 - Inf

Figure 36: Comparison of Results. Each subfigure shows from top-to-bottom: The disparity and optical flow ground truth in
the reference view, the disparity map (D1) and optical flow map (Fl) estimated by the specified algorithm, and the respective
error images using the color scheme depicted in the legend.

38

References[1] T. Brox and J. Malik. Large displacement optical flow: Descriptor matching in variational motion estimation. PAMI, 33:500–513,
March 2011. 4, 31, 32, 33, 34, 35, 36, 37, 38[2] J. Cech, J. Sanchez-Riera, and R. P. Horaud. Scene flow estimation by growing correspondence seeds. In CVPR, 2011. 4, 31, 32, 33,
34, 35, 36, 37, 38[3] A. Geiger, J. Ziegler, and C. Stiller. StereoScan: Dense 3D reconstruction in real-time. In IV, 2011. 2[4] H. Hirschmller. Stereo processing by semiglobal matching and mutual information. PAMI, 30(2):328–341, 2008. 4, 31, 32, 33, 34,
35, 36, 37, 38[5] M. Hornacek, A. Fitzgibbon, and C. Rother. SphereFlow: 6 DoF scene flow from RGB-D pairs. In CVPR, 2014. 4, 31, 32, 33, 34,
35, 36, 37, 38[6] F. Huguet and F. Devernay. A variational method for scene flow estimation from stereo sequences. In ICCV, 2007. 4, 5, 31, 32, 33,
34, 35, 36, 37, 38[7] D. Sun, S. Roth, and M. J. Black. A quantitative analysis of current practices in optical flow estimation and the principles behindthem. IJCV, 106(2):115–137, 2013. 4, 5
[8] L. Valgaerts, A. Bruhn, H. Zimmer, J. Weickert, C. Stoll, and C. Theobalt. Joint estimation of motion, structure and geometry fromstereo sequences. In ECCV, 2010. 5[9] C. Vogel, K. Schindler, and S. Roth. Piecewise rigid scene flow. In ICCV, 2013. 4, 5, 31, 32, 33, 34, 35, 36, 37, 38[10] A. Wedel, C. Rabe, T. Vaudrey, T. Brox, U. Franke, and D. Cremers. Efficient dense scene flow from sparse or dense stereo data. In
ECCV, 2008. 5

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