SlowGuess/NeurIPS_2023_OCR huggingface dataset:
4.2.2 Design choices for benchmarking ORE
Size of bounding box. Smaller bounding box should be more sensitive to errors (sec. 3.3). Indeed, we find higher correlation between ORE and ATE as we decrease bounding box size. Specifically, we expand bounding box sizes from  $20\%$  to  $1000\%$ , and observed decreasing Spearman Coef. and Kendall Coef. between ORE and  $\mathrm{ATE}_{rot} / \mathrm{ATE}_{trans}$ . This empirically verifies our intuition.
Relationship to geometric reprojection error. When the bounding box size reaches zero and collapses into a point, ORE effectively measures the reprojection error on this point tracklet, which was sometime referred to as (Geometric) Reprojection Error (GRE) [38, 24]. We implemented GRE metrics and found the ranking correlation to improve (albeit marginally) compared to small-sized bounding boxes (box size  $0\%$  vs. box size  $20\%$ ). Meanwhile, we remark several practical reasons for using static object tracklets over point tracklet annotations: a. easy to track: objects may survive under occlusion, large viewpoint change and motion blur, while points may lose track; b. object annotations are more accessible than point-tracking annotations, such as Ego4D [43], EPIC-VISOR [22], etc; c. object annotations require less efforts as point annotations are extremely time consuming [25].
Depth optimization finds optimal value. In sec 3.2, we remark that computing ORE relies on a step to optimize for depth value that minimizes ORE. We verify that this process indeed finds the correct depth value. When groundtruth camera trajectory is used, optimal depth is almost always recovered by minimizing ORE (fig. 3a). In addition, depth value found for a worse-performing method will also
Table 4: Smaller bounding box provides stronger ranking correlation for ORE with standard metrics.
Box Size(%)	1000	200	100	60	50	40	30	20	0
Spearman	vs. ATEtrans	0.262	0.520	0.559	0.693	0.692	0.718	0.719	0.716	0.720
vs. ATERot	0.384	0.593	0.701	0.759	0.763	0.772	0.788	0.800	0.800
Kendall τ	vs. ATEtrans	0.219	0.412	0.457	0.571	0.569	0.588	0.597	0.579	0.610
vs. ATERot	0.319	0.480	0.569	0.614	0.618	0.637	0.648	0.650	0.662
Figure 3: The ratio between optimized depth  $d^{*}$  and ground truth depth  $\hat{d}$  using (a) GT (b) COLMAP and (c) DROID trajectories. Minimizing ORE with GT trajectories almost always provide GT depth.
be less accurate. For example, minimizing ORE with COLMAP trajectories (fig. 3b) finds optimal depth in fewer sequences than better-performing DROID-SLAM (fig. 3c).
4.3 Experiments on EgoStatic
EgoTracks encompasses hundreds of scenarios from Ego4D and contains  $6000+$  unique sequences. Benchmarking on all videos from all scenes is infeasible, as many baselines may take very long time to finish (e.g. ParticleSfM and COLMAP may take up to 3 days). We believe users of EgoStatic shall choose scenarios of their interests and benchmark on a subset of sequences. Here, we select 18 most common scenarios from EgoStatic and summarize them into 11 categories, containing 30 video sequences. Each video is down-sampled to 0.25 of the original resolution (roughly 480p). We undistort the fisheye videos as most methods are designed for only pinhole camera models. We use 5 FPS following EgoTracks. We report aggregated results by taking average in each category (Table 5) and leave the per-sequence results in supplementary.
4.3.1 EgoStatic is challenging
As summarized in Table 5, all methods suffer from significant performance drop on EgoStatic compared to ScanNet. We remark an ORE greater than 0.2 often signals very noisy predictions (see supplementary). COLMAP is the best performing method, which indicates why it is often used in previous works requiring spatial relations in egocentric videos. However, its ORE is still relatively high, underscoring the difficulty. Like COLMAP, pure gemeotric approaches, such as ORBSLAM3, suffer from smaller performance drop. On the other hand, DROID-SLAM, the best method on ScanNet, scores 0.185. This highlights a potential difficulty for it to generalize, as it is trained on synthetic data in TartanAir [96]. In addition, we notice large variance in all methods in different sequences and categories. For example, all methods perform poorly on Social. Social often involves many moving people, increasing the difficulty to identify good static correspondences across frames.
4.3.2 What makes good camera trajectory predictor in EgoStatic?
In this section, we study the key factors contributing to a good camera pose estimation model for egocentric videos. Specifically, we revisit the design choices of SOTA models, COLMAP (geometry-based) and DROID-SLAM (learning-based). We demonstrate that ORE is sensitive to these factors, and therefore is a strong metric for future research.
Design choices in COLMAP. We study three versions of COLMAP: (a) Default uses the default automatic reconstruction pipeline; (b) Tuned replaces the exhaustive matching with vocabulary tree matching [75, 89]; (c) Mask filters dynamic components via hand-object masks from EgoHOS [102];
Table 5: Egostatic is challenging: all methods suffer vs. ScanNet. Trajectories with  $\mathrm{ORE} > 0.2$  are often very noisy; nearly all methods except COLMAP give very poor quality on EgoStatic.
DROID	COLMAP	ORB2	ORB3	Particle	Monodepth2	TartanVO
yardwork	0.110	0.038	0.105	0.066	0.189	0.332	0.296
mechanics	0.150	0.073	0.148	0.163	0.213	0.271	0.364
crafting	0.029	0.006	0.113	0.082	0.307	0.486	0.393
carpenter	0.265	0.017	0.167	0.153	0.334	0.335	0.369
lab	0.236	0.018	0.259	0.088	0.234	0.421	0.540
gardening	0.011	0.009	0.051	0.072	0.123	0.200	0.193
working	0.255	0.127	0.192	0.173	0.194	0.351	0.456
cooking	0.315	0.010	0.177	0.045	0.179	0.235	0.299
shopping	0.165	0.067	0.171	0.186	0.227	0.450	0.396
board game	0.194	0.030	0.118	0.141	0.355	0.425	0.375
social	0.220	0.186	0.224	0.168	0.116	0.331	0.421
Average	0.185	0.066	0.161	0.134	0.225	0.349	0.383
vs. ScanNet	+0.156	+0.031	+0.059	+0.025	+0.095	+0.098	+0.089
and (d) Distortion runs on original raw videos from Ego4D which includes severe distortion and fisheye effects. We summarized each version's performance in Table 6.
Compared to Default, Tuned achieves 0.041 ORE (-0.025). This suggests the practice introduced by [89] is indeed improving camera trajectory qualities. It is also worth noting Tuned runs about  $1.5\mathrm{x}$  faster than Default. Compared to Tuned, Distortion achieves 0.044 ORE (+0.003), which suggests that the internal fisheye camera model from COLMAP performs decently for in-the-wild Ego4D videos even without manual undistortion.
Design choices in DROID-SLAM. DROID-SLAM [87] performs recurrent iterative update operation of camera pose and pixelwise depth through a Dense Bundle Adjustment layer [87]. In the paper implementation, this update operation has been applied to both the local bundle adjustment in frontend and global bundle adjustment in backend. We used ORE metric on EgoStatic benchmark to quantify the relative gain from the addition of global bundle adjustment and see that ORE is able to capture the improvement induced by backend global bundle adjustment as in Table 7.
Table 6: Different version of COLMAP's performance on Egostatic.
(a) COLMAP-Default	(b) COLMAP-Tuned	(c) COLMAP-Mask	(d) COLMAP-Distortion
ORE	0.066	0.041	0.049	0.044
Table 7: Removing DROID-SLAM's global backend optimization module [87] decreases model performance on both Ego4d and ScanNet dataset.
EgoStatic-ORE	ScanNet-ORE	ScanNet-ATEtrans	ScanNet-ATERot
Frontend and Backend	0.185	0.029	0.147	0.135
Frontend Only	0.214	0.053	0.218	0.232
5 Discussions and limitations
We presented ORE, a metric to evaluate camera trajectories with lightweight tracking annotation. ORE removes the need to collect groundtruth camera trajectories and uses object tracklets as a proxy. By applying ORE to Ego4D, we construct EgoStatic, a new large-scale benchmark for egocentric videos. We show that estimating camera trajectories in real-world egocentric videos is very challenging: existing approaches and their simple variants do not offer satisfying solutions. We hope that ORE can give researchers the convenience to study camera poses in more diversified scenarios, and EgoStatic to serve as a new playground to improve camera localization in egocentric videos.
Limitations. We remark that ORE is complementary to standard metrics computed with groundtruth camera pose, and is not meant to replace them; we feel the right level of spatial detail depends on the task, and view the proposed benchmark as highly compatible with more accurate metric methods. It should serve as a way to enrich current evaluation sets with more diversity of videos, especially when groundtruth is difficult to acquire, or video capture has already occurred. As learned methods become more commonplace, increased visual diversity becomes ever more valuable. We see the current work as a step towards ways that semantics and geometry can be measured in a more coherent fashion.
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A Supplementary List
We include the following contents in the supplementary materials,
1. supplemental.pdf: This PDF contains more experiments/ablations, visualizations as well as information about EgoStatic dataset.
2. visualization_1.mp4, visualization_2.mp4 and visualization_3.mp4: visualization videos of EgoStatic benchmark where colored bounding boxes are groundtruth tracklet annotations, colored dots on the left are reprojected points tracked by estimated camera trajectory and green dots on the right are visualization of estimated camera trajectory (from COLMAP[33]).
3. egostatic.zip: This repo contains (a) code snippets for ORE evaluation on EgoStatic and (b) sample pose files from 7 baseline methods. Detailed instruction is included in README.md.
B Experiments on ScanNet (Continued)
ScanNet [21] is an RGB-D video dataset containing more than 1500 3D scans, each with 3D camera pose annotations and point-clouds. We pick high confidence instance mask from Mask-RCNN [47], and construct object tracklets by back-projecting masks to the point cloud. We benchmark methods on 20 test sequences using  $\mathrm{ATE}_{trans}$ ,  $\mathrm{ATE}_{rot}$  and  $\mathrm{ORE}$ , and summarized the per-scene breakdown in Table 8, 9 and 10.
Table 8: ORE performance of 7 methods on ScanNet dataset
DROID	COLMAP	ORB2	ORB3	Particle	TartanVO	Monodepth2
scene0707_00	0.000	0.000	0.002	0.001	0.057	0.149	0.276
scene0708_00	0.169	0.066	0.357	0.060	0.169	0.176	0.206
scene0709_00	0.000	0.035	0.034	0.017	0.129	0.155	0.230
scene0710_00	0.013	0.012	0.069	0.033	0.336	0.533	0.366
scene0711_00	0.004	0.004	0.232	-	0.203	0.374	0.460
scene0712_00	0.186	0.062	0.143	0.141	0.292	0.409	0.569
scene0713_00	0.000	0.000	0.211	0.402	0.264	0.362	0.411
scene0714_00	0.001	0.001	-	0.001	0.004	0.123	0.237
scene0715_00	0.000	0.000	0.130	0.000	0.024	0.076	0.321
scene0716_00	0.000	0.006	0.000	0.000	0.000	0.083	0.083
scene0717_00	0.000	0.000	0.002	-	0.000	0.070	0.103
scene0718_00	0.000	0.000	-	0.163	0.002	0.079	0.069
scene0719_00	0.083	0.082	0.085	0.078	0.113	0.090	0.224
scene0720_00	0.000	0.170	0.013	0.185	0.261	0.532	0.436
scene0721_00	0.077	0.204	0.128	0.186	0.114	0.369	0.425
scene0722_00	0.002	0.002	0.074	0.085	0.079	0.194	0.311
scene0723_00	0.040	0.040	0.077	0.381	0.098	0.542	0.641
scene0724_00	0.001	0.001	0.115	0.123	0.314	0.237	0.201
scene0725_00	0.007	0.006	0.161	-	0.130	0.209	0.112
scene0726_00	0.000	0.000	0.012	0.002	0.003	0.258	0.202
Average	0.029	0.035	0.102	0.109	0.130	0.251	0.294
STD	0.055	0.057	0.093	0.122	0.111	0.159	0.155
C Extended results on ranking statistics
In Section 4.2, we used two ranking statistics to capture the relationship between ORE and standard metrics: Spearman's rank correlation coefficient (Spearman Coef.) [100] and Kendall's  $\tau$  coefficient (Kendall Coef.) [1].
Table 9:  $\mathbf{{ATE}}_{\text{trans }}$  performance of 7 methods on ScanNet dataset
DROID	COLMAP	ORB2	ORB3	Particle	TartanVO	Monodepth2
scene0707_00	0.052	0.111	0.123	0.111	0.184	0.589	0.851
scene0708_00	0.638	0.380	1.497	0.380	0.233	0.389	1.196
scene0709_00	0.060	0.455	0.110	0.455	0.237	0.385	0.737
scene0710_00	0.070	0.167	0.102	0.167	0.271	0.395	0.549
scene0711_00	0.049	0.393	0.632	-	0.708	0.888	0.928
scene0712_00	0.636	0.656	0.194	0.393	0.570	0.787	0.698
scene0713_00	0.199	0.156	0.612	0.656	0.580	0.538	0.630
scene0714_00	0.040	0.108	-	0.156	0.497	0.443	0.782
scene0715_00	0.057	0.669	0.500	0.108	0.243	0.122	0.483
scene0716_00	0.529	0.455	0.647	0.669	0.527	0.222	0.867
scene0717_00	0.072	0.038	0.243	-	0.253	0.374	0.529
scene0718_00	0.170	0.995	-	0.455	0.256	0.092	0.230
scene0719_00	0.026	2.262	0.067	0.038	0.132	0.184	0.628
scene0720_00	0.053	0.537	0.100	0.995	0.797	0.635	0.943
scene0721_00	0.098	0.878	1.380	2.262	1.399	0.813	2.200
scene0722_00	0.034	0.744	0.501	0.537	0.529	0.246	0.492
scene0723_00	0.055	0.058	0.121	0.878	0.544	0.561	0.830
scene0724_00	0.031	0.001	0.527	0.744	0.713	0.478	0.743
scene0725_00	0.039	0.006	0.857	-	0.799	0.613	0.908
scene0726_00	0.039	0.000	0.111	0.058	0.216	0.270	0.568
Average	0.147	0.331	0.462	0.533	0.484	0.451	0.790
STD	0.196	0.468	0.420	0.518	0.298	0.222	0.385
Table 10:  $\mathbf{{ATE}}_{rot}$  performance of 7 methods on ScanNet dataset
DROID	COLMAP	ORB2	ORB3	Particle	TartanVO	Monodepth2
scene0707_00	0.035	0.066	0.094	0.097	0.176	2.403	1.251
scene0708_00	0.783	0.079	1.564	0.164	0.158	2.271	1.926
scene0709_00	0.029	1.134	0.086	0.178	0.220	2.265	1.586
scene0710_00	0.058	0.057	0.203	0.231	0.449	1.704	2.382
scene0711_00	0.033	0.204	1.068	-	0.976	1.962	2.251
scene0712_00	0.450	0.829	0.233	0.308	1.836	1.918	2.096
scene0713_00	0.211	0.077	1.573	2.527	0.581	2.299	2.646
scene0714_00	0.041	0.055	-	0.147	0.154	2.118	1.044
scene0715_00	0.056	0.100	1.638	0.105	0.295	2.105	0.814
scene0716_00	0.296	0.784	1.557	0.238	0.458	2.156	1.165
scene0717_00	0.033	0.071	0.116	-	0.266	2.018	1.087
scene0718_00	0.226	0.225	-	1.899	1.089	2.049	0.606
scene0719_00	0.051	0.058	0.118	0.063	0.104	1.998	1.937
scene0720_00	0.049	1.438	0.197	1.995	0.633	2.155	2.582
scene0721_00	0.065	2.824	1.966	2.747	0.842	2.038	1.982
scene0722_00	0.034	0.037	0.746	2.407	0.673	1.945	1.717
scene0723_00	0.051	0.056	0.146	2.551	0.328	2.069	2.746
scene0724_00	0.050	0.135	2.349	2.359	1.806	2.020	1.906
scene0725_00	0.086	0.591	2.352	-	1.266	2.343	2.605
scene0726_00	0.060	0.134	0.114	0.084	0.266	2.081	2.899
Average	0.135	0.448	0.896	1.065	0.629	2.096	1.861
STD	0.184	0.676	0.830	1.096	0.513	0.161	0.669
Spearman's rank correlation coefficient Given  $n$  raw scores  $X_{i}$  and  $Y_{i}$ , ranking function  $R(\cdot)$
$$
\rho_ {R (x), R (Y)} = \frac {\operatorname {c o v} (R (X) , R (Y))}{\sigma_ {R (X)} \sigma_ {R (Y)}} \tag {6}
$$
where  $\operatorname{cov}(R(X), R(Y))$  is the covariance,  $\sigma_{R(X)}$  and  $\sigma_{R(Y)}$  are standard deviations.
We compute Spearman's ranking statistics on each scene in Table 11, then average across all scenes.
Table 11: Spearman Coef. and Kendall Coef. for ScanNet. Here we show Spearman and Kendall Coef. between (a) ORE and  $\mathrm{ATE}_{trans}$ , (b) ORE and  $\mathrm{ATE}_{rot}$ , as well as (c)  $\mathrm{ATE}_{trans}$  and  $\mathrm{ATE}_{rot}$  for each scene in ScanNet test set. The table is summarized as Table 3 of main paper.
Spearman Coef.	Kendall Coef.
ORE	vs. ATEtrans	vs. ATErot	ATEtrans vs ATErot	vs. ATEtrans	vs. ATErot	ATEtrans vs ATErot
scene0707_00	0.964	0.893	0.929	0.905	0.714	0.810
scene0708_00	0.857	0.750	0.750	0.714	0.524	0.619
scene0709_00	0.607	0.893	0.714	0.524	0.714	0.619
scene0710_00	0.893	0.929	0.964	0.714	0.810	0.905
scene0711_00	0.964	1.000	0.964	0.905	1.000	0.905
scene0712_00	0.643	0.750	0.821	0.429	0.619	0.619
scene0713_00	0.786	0.929	0.857	0.524	0.810	0.714
scene0714_00	0.857	0.857	0.893	0.714	0.714	0.810
scene0715_00	0.889	0.815	0.786	0.720	0.617	0.714
scene0716_00	0.039	0.670	-0.214	0.056	0.620	-0.238
scene0717_00	0.889	0.852	0.929	0.823	0.720	0.810
scene0718_00	0.721	0.919	0.643	0.586	0.781	0.619
scene0719_00	0.821	0.679	0.929	0.619	0.429	0.810
scene0720_00	0.679	0.857	0.750	0.524	0.714	0.619
scene0721_00	0.464	0.679	0.607	0.333	0.524	0.429
scene0722_00	0.464	0.821	0.643	0.429	0.619	0.429
scene0723_00	0.857	0.929	0.964	0.714	0.810	0.905
scene0724_00	0.536	0.321	0.714	0.333	0.143	0.619
scene0725_00	0.643	0.714	0.964	0.429	0.524	0.905
scene0726_00	0.739	0.739	1.000	0.586	0.586	1.000
Average (STD)	0.716 (± 0.216)	0.800 (± 0.145)	0.780 (± 0.216)	0.579 (± 0.205)	0.650 (± 0.173)	0.681 (± 0.261)
Kendall's  $\tau$  rank correlation coefficient measures for ordinal association between two sets of data. Given  $\{R(X_i)\}_{i=1}^n$  and  $\{R(Y_i)\}_{i=1}^n$ ,
a pair of random variables  $(X,Y)$ , where  $X = \{x_{1},x_{2},\ldots x_{n}\}$  and  $Y = \{y_{1},y_{2},\ldots y_{n}\}$ . If either both  $x_{i} > x_{j}$  and  $y_{i} > y_{j}$  or both  $x_{i} < x_{j}$  and  $y_{i} < y_{j}$  holds, the pair  $(x_{i},y_{i})$  and  $(x_{j},y_{j})$  are said to be concordant; otherwise they are said to be discordant. Assume for random variable  $(X,Y)$ , there exists  $A$  concordant pairs and  $B$  discordant pairs, the Kendall  $\tau$  coefficient is defined as  $\tau = \frac{A - B}{\binom{n}{2}}$ .
We consider  $(X,Y)$  as the performance of two different methods on the same scene, resulting in 420 such pairs across 20 scenes in ScanNet.
C.1 Relationship with RPE
We summarize the ranking statistics between ORE and RPE in Supp. Tab 12. We observe despite ORE has strong correlation with RPE, it is weaker than the correlation with ATE. This is expected, since both ORE and ATE measure the quality of the entire trajectory, whereas RPE measures a small local window. In addition, similar to ATE, we observe ORE to have stronger correlation with rotation compared to translation.
C.2 Inconsistent pairs removal in Kendall's  $\tau$
As discussed in the section 4.2 in the main paper, standard metrics  $ATE_{trans}$  and  $ATE_{rot}$  may often disagree with each other. This indicates for two methods, one may perform better on translation while the other performs better on rotation. This limits ORE's ranking statistic values to have an upper bound: since ORE factors in both translation and rotation, it can agree with either  $ATE_{trans}$  or  $ATE_{rot}$  when  $ATE_{trans}$  and  $ATE_{rot}$  mismatch. We capture this problem by removing such pairs in Kendall's  $\tau$ . In total, we remove  $16\%$  of such pairs.
As summarized in Supp. Tab. 13, ORE and ATE has extremely high Kendall ranking coefficients. This implies only  $5\%$  of the pairs may disagree between ORE and ATE. ORE also achieves strong correlation with RPE, but is weaker than the global ATE measurement.
Table 12: ORE's ranking correlation with RPE. Spearman Coef. (upper triangle) and Kendall Coef. (lower triangle) between ORE and standard metrics.
Spearman
Kendall	ORE	RPEtrans	RPErot
ORE	-	0.425	0.750
RPEtrans	0.334	-	0.473
RPErot	0.615	0.386	-
Table 13: ORE achieves very strong correlation with ATE and RPE with inconsistent pairs removal.
ATE	RPE
Kendall	0.895	0.571
In this section, we provided more visualization of the ORE metrics and its associated EgoStatic benchmark.
Visualization for ORE metrics For each of the 7 baseline methods, we plot the estimated trajectory along with the ground truth trajectory, as well as its associated ORE,  $\mathrm{ATE}_{trans}$  and  $\mathrm{ATE}_{rot}$  metric. From Figure 4, we can see both qualitatively and quantitatively that ORE correlated highly with the quality of estimated camera trajectory.
Visualization for EgoStatic Benchmark In Figure 5, we visualized 24 sampled screenshot from 12 arbitrary scenes in EgoStatic benchmark (each with 2 images). The scenarios span across various categories including cooking, crafting, yardwork, gardening, lab, board game etc, which demonstrate the diverse scenes and complex actions EgoStatic is able to cover.
E Annotation Limitations
The annotation for whether an object is static was done by human labelers. Although quality check has been placed for all jobs, it is prone to human bias and error. Also, the videos are sourced from Ego4D VQ benchmark, so it may not represent the entire data distribution of Ego4D.
F Potential negative societal impacts
Since our videos are sourced from Ego4D and EgoTracks, we inherit many of the potential negative impacts. Details are discussed in the original paper [43] Appendix K. We summarize several here:
- There may be risks surrounding privacy, such as personnel being recorded during the video. Consent was obtained in the original dataset, and a user agreement is enforced for Ego4D. Our dataset follows the same protocol as Ego4D.
- The existing efforts may inspire future data collection with less attention to privacy and ethics. Best practices were detailed in the original paper [43]. We did the same with our paper to include the instructions to help mitigate this risk.
- There may be data imbalances, such as geographical distribution. This risk can be mitigated with future work that grows the collaboration in underrepresented areas.
Scene0721_00
ORE: 0.077
ATE(trans): 0.098
ATE(rot): 0.065
COLMAP
ORE: 0.204
ATE(trans): 0.878
ATE(rot): 2.824
ParticleSfM
ORE: 0.114
ATE(trans): 1.399
ATE(rot): 0.842
TartanVO
ORE:0.369
ATE(trans): 0.813
ATE(rot): 2.038
ORBSLAM2
ORE:0.128
ATE(trans): 1.380
ATE(rot): 1.966
Monodepth2
(on KITTI)
ORE:0.425
ATE(trans): 2.200
ATE(rot):1.982
ORBSLAM3
ORE:0.186
ATE(trans): 2.262
ATE(rot): 2.747
Scene0723_00
DROID-SLAM
ORE:0.040
ATE(trans):0.055
ATE(rot): 0.051
COLMAP
ORE:0.040
ATE(trans): 0.058
ParticleSfM
ORE:0.098
ATE(trans): 0.544
ATE(rot): 0.328
Figure 4: Visualization of the camera trajectory The ORE,  $\mathrm{ATE}_{trans}$  and  $\mathrm{ATE}_{rot}$  metric of 7 methods discussed in Sec 4.1 are listed below the trajectory to show that ORE highly correlates with  $\mathrm{ATE}_{trans}$  and  $\mathrm{ATE}_{rot}$  in its capability to describe the trajectory quality.
ATE(rot): 0.056
TartanVO
ORE:0.542
ATE(trans):0.561
ATE(rot): 2.069
ORBSLAM2
0.077
ATE(trans):0.121
ATE(rot): 0.146
Monodepth2
(on KITTI)
ORE: 0.641
ATE(trans):0.830
ATE(rot): 2.746
ORBSLAM3
ORE:0.381
ATE(trans): 0.878
ATE(rot): 2.551
Figure 5: Visualization of EgoStatic benchmark We include 24 screenshots from 12 videos in the proposed EgoStatic benchmark. Colored bounding boxes are ground truth tracklet annotations; dots are reprojected points tracked by estimated camera trajectory. Ideally, the points should fall in the bounding boxes.
G Data sheet
We follow [35] for writing the data sheet of EgoStatic.