SlowGuess/NeurIPS_2023_OCR huggingface dataset:
Notably, as our method learns to explicitly model independent motion, there is no filtering based on camera velocity recordings when constructing the training dataset.
Figure 7: Histogram of the number of moveable objects per frame for all three dataset.
For the KITTI Dataset [9], we follow the Eigen split [6] with 39,180 image-triplets used for training and 697 images used for evaluation. During training, frames are downsampled to  $640 \times 192$ .
Finally, we plot the histogram of the number of moveable objects per frame for all three datasets. Shown in Figure 7, Waymo Open Dataset has the most moveable objects with a mean of 12.12, followed by nuScenes with a mean of 7.78. Due to the lack of per-frame object labels in KITTI, we evaluate on its curated object detection dataset, which has a mean of 5.26. We note that the real distribution should be much tighter near 0, compared to the detection dataset with all images containing at least 1 moveable object.
Table 5: Additional depth evaluation on the KITTI Dataset. Stereo Train and Stereo Infer: stand for the use of stereo-view during training and inference, respectively. Multi-Frame denotes the use of multiple frames as inputs during inference. Add. Sup. stands for additional supervision during training where I, S, and D stand for IMU-level, synthetic-data, and pretrained depth model supervision, respectively.
Stereo	Multi- Frame	Add. Sup.	Error metric (↓)	Accuracy metric (↑)
Train	Infer.	Abs Rel	Sq Rel	RMSE	RMSE log	δ < 1.25	δ < 1.252	δ < 1.253
UnOS [38]	✓	✓	0.049	0.515	3.404	0.121	0.965	0.984	0.992
EffiScene [18]	✓	✓	0.049	0.522	3.461	0.120	0.961	0.984	0.992
Xiang et al. [41]	✓	✓	0.048	0.487	3.447	0.117	0.964	0.985	0.992
Liu et al. [26] (stereo)	✓	✓	0.051	0.532	3.780	0.126	0.957	0.982	0.991
Liu et al. [26] (mono)	✓	0.108	1.020	5.528	0.195	0.863	0.948	0.980
Hur et al. [16]	✓	0.125	0.978	4.877	0.208	0.851	0.950	0.978
PLADE-Net [11]	✓	0.089	0.590	4.008	0.172	0.900	0.967	0.985
DRAFT [13]	✓	S	0.097	0.647	3.991	0.169	0.899	0.968	0.984
Dyna-DepthFormer [46]	✓	0.094	0.734	4.442	0.169	0.893	0.967	0.983
ManyDepth [40]	✓	0.090	0.713	4.261	0.170	0.914	0.966	0.983
DepthFormer [25]	✓	0.090	0.661	4.149	0.175	0.905	0.967	0.984
Zhang et al. [45]	I	0.108	0.761	4.608	0.187	0.883	0.962	0.982
SC-DepthV3 [33]	D	0.118	0.756	4.709	0.188	0.864	0.960	0.984
Dynamo-Depth	0.112	0.758	4.505	0.183	0.873	0.959	0.984
Table 6: Depth evaluation on the nuScenes Dataset with day-clear split.  $IM$  stands for independent motion where  $\times$  denotes a lack of independent motion modeling. Sup. indicates additional supervision given during training, where 'V' indicates camera velocity recordings and 'R' indicates radar scans. Manual replication with released code is indicated by  ${}^{ \dagger  }$  .
IM Sup.	Error metric (↓)	Accuracy metric (↑)
Abs Rel	Sq Rel	RMSE	RMSE log	δ < 1.25	δ < 1.252	δ < 1.253
Monodepth2† [10]	X	0.157	2.432	6.943	0.248	0.844	0.940	0.968
LiteMono† [44]	X	0.161	2.618	6.818	0.251	0.846	0.939	0.966
R4Dyn-L [8]	V,R	0.130	1.658	6.536	-	0.858	-	-
Dynamo-Depth (MD2)	0.149	1.792	6.536	0.236	0.830	0.938	0.972
Dynamo-Depth	0.132	1.571	6.158	0.216	0.856	0.951	0.977
Table 7: Depth evaluation with semantic split on the nuScenes Dataset with day-clear split. S.B., S.O. and M.O. denotes the partition of pixels that are static background, static moveable object and moving object, respectively. Manual replication with released code is indicated by  $\dagger$ .
Abs Rel (↓)	δ < 1.25 (↑)
All	S.B.	S.O.	M.O.	All	S.B.	S.O.	M.O.
Monodepth2† [10]	0.157	0.150	0.214	0.324	0.844	0.850	0.774	0.626
Dynamo-Depth (MD2)	0.149	0.146	0.186	0.197	0.830	0.834	0.779	0.737
Δ (%)	-5.1	-2.7	-13.1	-39.2	-1.7	-1.9	+0.6	+17.7
LiteMono† [44]	0.161	0.151	0.232	0.373	0.846	0.853	0.781	0.572
Dynamo-Depth	0.132	0.130	0.168	0.167	0.856	0.857	0.795	0.804
Δ (%)	-18.0	-13.9	-27.6	-55.2	+1.2	+0.5	+1.8	+40.6
D.1 Additional KITTI Comparisons
Please refer to Table 5 for additional comparisons on KITTI, where the compared methods leverage either additional modalities or additional supervision beyond the use of semantic information as shown in Table 1.
D.2 Additional nuScenes Comparisons
Please refer to Table 6 and Table 7 for depth performance comparisons on the nuScenes validation day-clear subset, along with precision-recall curve for motion segmentation in Figure 8. Note that R4Dyn-L [8] is only trained on the day-clear subset with velocity filtering, while the rest of the methods are all trained with the entire dataset without any filtering. Furthermore, Table 7 still demonstrates the trend as observed in Table 2, where the a significant improvement on moving objects are observed in terms of both accuracy and error. Finally, as shown in Figure 9, both methods fail under low-light conditions where the brightness consistency assumption no longer holds. Due to the rigid scene constraint, artifacts appear in the respective depth prediction in (b), which causes a much larger error compared to the prediction in (d), as discussed in Section 5.1.
Figure 8: Precision-Recall curve for motion segmentation on the nuScenes Dataset, which is partitioned into the day-clear and other subsets.
Figure 9: Visualizing predictions under night-time conditions in the nuScenes dataset.
D.3 Odometry Evaluation
Please refer to Table 8 for odometry evaluation for Dynamo-Depth and Dynamo-Depth (MD2) on both nuScenes day-clear subset and Waymo Open Dataset.
Table 8: Odometry results on the nuScenes (day-clear subset) and Waymo Open Dataset. Results show the average absolute trajectory error over all overlapping 5-frame snippets and 100-frame snippets in the test sequences, and standard deviation, in meters. For consistency, the first 100 test sequences for both datasets are evaluated. Manual replication with released code is indicated by  $\dagger$ .
Dataset	nuScenes	Waymo
Speed (m/s)	5.657 ± 4.188	6.978 ± 6.028
Snippet Length (# frames)	5	100	5	100
Snippet Duration (seconds)	0.3	7.4	0.4	9.9
Snippet Counts	22820	13320	19364	9864
Monodepth2† [10]	0.016 ± 0.017	0.097 ± 0.069	0.018 ± 0.029	0.143 ± 0.194
Dynamo-Depth (MD2)	0.019 ± 0.029	0.112 ± 0.160	0.015 ± 0.027	0.091 ± 0.121
LiteMono† [44]	0.018 ± 0.018	0.090 ± 0.065	0.022 ± 0.044	0.156 ± 0.194
Dynamo-Depth	0.017 ± 0.019	0.081 ± 0.070	0.017 ± 0.036	0.107 ± 0.164
D.4 Scene Flow Comparisons
We were not able to compare against the mentioned related works in unsupervised scene flow estimation due to the large difference in methodology (stereo-view during training [18, 41, 16, 17]) and lack of codebase for reproducibility [13].
As shown in Figure 10, dynamical shadows and moving light sources in low-light conditions violate the brightness consistency assumption, which results in erroneous predictions of motion segmentation and monocular depth, respectively.
Figure 10: Limitations of Dynamo-Depth.