SlowGuess/NeurIPS_2023_OCR huggingface dataset:
David Recasens  
University of Zaragoza
Martin R. Oswald  
ETH Zurich, University of Amsterdam
Marc Pollefeys  
ETH Zurich, Microsoft
Javier Civera  
University of Zaragoza
Estimating camera motion in deformable scenes poses a complex and open research challenge. Most existing non-rigid structure from motion techniques assume to observe also static scene parts besides deforming scene parts in order to establish an anchoring reference. However, this assumption does not hold true in certain relevant application cases such as endoscopies. Deformable odometry and SLAM pipelines, which tackle the most challenging scenario of exploratory trajectories, suffer from a lack of robustness and proper quantitative evaluation methodologies. To tackle this issue with a common benchmark, we introduce the Drunkard's Dataset, a challenging collection of synthetic data targeting visual navigation and reconstruction in deformable environments. This dataset is the first large set of exploratory camera trajectories with ground truth inside 3D scenes where every surface exhibits non-rigid deformations over time. Simulations in realistic 3D buildings let us obtain a vast amount of data and ground truth labels, including camera poses, RGB images and depth, optical flow and normal maps at high resolution and quality. We further present a novel deformable odometry method, dubbed the Drunkard's Odometry, which decomposes optical flow estimates into rigid-body camera motion and non-rigid scene deformations. In order to validate our data, our work contains an evaluation of several baselines as well as a novel tracking error metric which does not require ground truth data. Dataset and code: https://davidrecasens.github.io/TheDrunkard'sOdometry/
Deformable scenes are among the most challenging cases for visual navigation and multi-view reconstruction. They may also be among the ones with the most relevant potential applications, ranging from the reconstruction of deforming objects [1], faces [2], hands [3], human bodies [4] (or animals' [5]), clothing [6]) or the body interior for medical applications [7, 8, 9, 10]. Among all potential applications for mapping and navigation in non-rigid scenes, medical ones stand out as very different from the rest and are the target of our work. In certain medical procedures, such as endoscopies, a camera navigates inside the human body, performing exploratory trajectories that extend far beyond its field of view. For the rest of the applications mentioned, the camera remains nearly or fully stationary and most views have a high degree of overlap. The field of Non-Rigid Structure from Motion (NRSfM) has experienced significant progress in the last decades, e.g. [11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24]. However, most of them address small-scale reconstructions and are of limited use in medical applications. For the few exceptions that cover SLAM [25] in deformable scenes (e.g., [7, 8, 10, 26]), there are no clear benchmarks nor datasets to support and track progress in the field.
Figure 1: Sample scene of the Drunkard's Dataset. The dataset provides various levels of scene deformation. Top row: Sample frames from Scene 0 over all difficulty levels  $0 - 3$ . Bottom row: External views showing the ground truth camera trajectory in green and the camera frame in purple. With increasing deformation level the camera motion is more abrupt. See animations with Adobe Reader, KDE Okular or YouTube.
Our main contribution in this work is motivated by this need for benchmarking exploratory camera motion estimations in deforming scenes. Publicly available datasets imaging deforming scenes do not cover exploratory trajectories, and the ones that do cover them do not have ground truth geometric annotations. Our proposal is a synthetic dataset that we denote as the "Drunkard's Dataset", containing a set of high-resolution RGB images, ground truth depth, optical flow, normal maps and camera pose trajectories in synthetic but texture-realistic deforming scenes. In order to generate a sufficient amount and variety of data, we imported the real-world scanned indoor 3D models of the Habitat-Matterport 3D dataset [27], added dynamic deformations, and generated trajectories within them. Figure 1 shows several data samples with the camera trajectory that advances over time. As the original 3D models are real-world scans, camera trajectory and depth are in consistent metric scale along scenes. To make it a scalable benchmark dataset, every scene was recorded under four different levels of reconstruction difficulty, increasing the deformation and camera trajectory perturbations (observe them in Figure 1). The Drunkard's Dataset is unique in its kind, providing large-scale data in deformable scenes, which will enable both, benchmarking non-rigid navigation and reconstruction methods, as well as sufficient data to evaluate the potential benefits of deep learning. Please visit our project website for further details and access to the dataset and source code.
Scientific progress is in many occasions based on well-established and public benchmarks. This has been the specific case in computer vision research in the last decades, up to the point that the existence of some benchmarks is highly correlated with scientific progress in the field. Data repositories are also essential nowadays not only to benchmark different methods, but also to train deep neural networks. However, collecting large amounts of data with non-rigid content is challenging due to difficulties in annotation, as argued by Li et al. [2]. Indeed, some very popular datasets are synthetic [28, 29].
Capturing large-scale data in sufficiently large non-rigid spaces, in order to benchmark odometry/SLAM methods, is even more challenging. Having ground truth annotations, in particular in the medical domain, hugely increases the challenge. The most popular medical datasets [30, 31, 32, 33] are small, lack geometric ground truth, or both. As a result, comparisons between methods are very often inaccurate, inconclusive or questionable. This motivates our work.
The second contribution of this paper is the Drunkard's Odometry (see Figure 2), a flow-based odometry method for camera motion estimation from RGB-D sequences in deformable scenes. Our method is inspired by the pose estimation of DROID-SLAM [34] and by the 3D scene flow prediction of RAFT-3D [35]. Our novelty compared to both is that our architecture models potential scene deformations.
Figure 2: Overview of the Drunkard's Odometry pipeline.
As third contribution, in order to validate methods also in non-annotated data, we present a novel metric, the Absolute Palindrome Trajectory Error (APTE). Our novel metric is based on running odometry methods forwards and backwards in a sequence of images, and comparing the pose errors between the first and last frames through different loop lengths. This metric may be useful for validating methods in realistic setups, but it is limited to relative errors between two poses, and hence it is not as informative as metrics using ground truth labels. For this reason, we believe that APTE in real sequences should not be trusted on its own, but as a complement to a larger and more informative set of metrics in our simulated Drunkard's Dataset.
Structure from Motion, visual odometry and visual SLAM methods for rigid scenes are commonly evaluated in a well established set of publicly available datasets [42, 43, 44, 45, 46, 47, 48], with ground truth geometric annotations and under the same metrics, which facilitates comparisons between them and progress in the field. Large-scale annotations for geometric ground truth are quite challenging in real scenarios, and still contain small errors due to the accuracy of the equipment (GPS
Dataset	Sim/Real	GT	Explo- ratory	# Frames	Domain
De Aguiar et al. [36]	Real	✓	✘	~ 6K	Human bodies
FAUST [37]	Real	✓	✘	~ 9K	Human bodies
NRSfM Challenge [32]	Real	✓	✘	-	Objects
DeformingThings4D [38]	Sim	✓	✘	~ 122K	Humans & animals
ToFu [2]	Sim	✓	✘	~ 20K	Faces
EndoMapper [33]	Real	✘	✓	~ 4M	Colonoscopies
Hamlyn [39, 30, 40, 41]	Real	✘	✓	~ 93K	Laparoscopies
Drunkard's (ours)	Sim	✓	✓	~ 416K	Indoor scenes
Table 1: Overview of existing non-rigid dataset specifications compared to our Drunkard's dataset.
or motion capture systems) used. An accurate approximation of the depth and camera pose could be recorded with sensors, but could not obtain such a good quality of optical flow and normal maps. In real-world applications that involve significant deformations, such as endoscopies, these sensors cannot be even equipped. While we could attempt to simulate a small-scale scenario using moving blankets or bouncy castles, it would fall short in terms of capturing a wide range of textures, deformations, and camera trajectories. Virtual environments, on the other hand, offer a superior solution, providing a rich and diverse representation of the complex conditions that are found in real-world settings. Synthetic datasets solve these issues at the price of the sim-to-real gap, and are common for benchmarking methods on navigation and reconstruction on rigid scenes [49, 50, 51, 52, 27]. For similar reasons, synthetic datasets are widely used in other computer vision tasks, such as stereo and flow [53, 28, 54, 29, 55, 56], depth [57, 58], object recognition and segmentation [59], object pose estimation and tracking [60], and scene segmentation, understanding and reasoning [61, 62, 63]. For additional insights and references on the use of synthetic data in deep learning, the reader is referred to the excellent survey by Nikolenko [64].
Note that, in most of the datasets cited, in particular those for faces and humans bodies, the camera is almost stationary. For the few datasets where the camera moves sufficiently, exploring new areas, the covered region is in any case reduced, deformations are small or there is absence of geometric
ground truth (see Table 1). This poses difficulties for benchmarking methods for odometry and SLAM targeting non-rigid environments, such as [7, 9]. Odometry and SLAM methods for deformable scenes are scarce in the literature, being [65, 7, 8, 9, 26, 10, 66, 67] the most representative ones. However, they are all based on feature matching or direct tracking, which make them unstable in challenging sequences. Differently, our Drunkard's Odometry is based on scene flow, which makes it significantly more robust.
3 The Drunkard's Dataset
Figure 3: Sample images of the Drunkard's Dataset with their corresponding depth, optical flow and normal maps. Non-rigid deformations are simulated by smooth deformations of all scene parts.
The Drunkard's Dataset is a publicly available set of 19 different camera trajectory recordings of 19 different deforming indoor scenes, where each one has been recorded four times, one for each difficulty level. For each level, we generated over  $100\mathrm{K}$  frames and recorded camera poses, RGB images, depths, optical flow and normal maps at  $1920 \times 1920$ , being the camera poses and the depth in consistent real-world metric scale throughout different scenes. Find sample images in Figure 3.
We used Blender [68] to render the deformations of the 19 real-world scanned indoor 3D models of the Habitat-Matterport 3D dataset [27]. We manually designed camera trajectories such that every room in each building is visited once and the camera trajectory returns at the starting point, except for scenes 4, 9 and 14, in which the camera traverses the building three times, but in each loop visiting the rooms in different order. The Blender files are publicly available, along with the scripts we used for generating them, so that anyone can render in-house versions of the Drunkard's Dataset scenes modifying parameters of deformations, pose trajectory, resolution or camera type among others.
As Table 2 details, level 0 stands for zero deformation and camera noise, resulting in a rigid scene and a smooth camera motion, well suited for rigid SfM/SLAM methods. The following levels have an increasing degree of deformation and trajectory noise. Having four levels of difficulty allows to benchmark both rigid and non-rigid methods in a graduated manner.
Difficulty level	Deformations	Trajectory noise
Level 0	0	0
Level 1	Low	Low
Level 2	Medium	Medium
Level 3	High	High
Table 2: Overview of the four difficulty levels. Deformation and camera trajectory noises increases with higher levels. At level 0 there is neither deformation nor camera noise, i.e. it represents a rigid scene and a smooth motion.
4 The Drunkard's Odometry
Given a pair of RGB-D images  $\{\mathbf{I}_i,\bar{\mathbf{Z}}_i\}$ ,  $\mathbf{I}_i\in \mathbb{R}^{w\times h\times 3}$  and  $\bar{\mathbf{Z}}_i\in \mathbb{R}^{w\times h}$ ,  $i\in \{1,2\}$ , our Drunkard's Odometry estimates a dense scene flow  $\mathbf{T}^k\in \mathrm{SE}(3)^{w\times h}$  between them, and the relative camera motion  $\mathbf{T}_c^k\in \mathrm{SE}(3)$  iteratively ( $k\in \{1,\dots,M\}$  stands for the iterative block step).  $\mathbf{T}^k$  contains pixel-wise rigid-body transformations that ideally, i.e. in absence of noise, maps every 3D point  $\mathbf{P}_j = \pi^{-1}(\mathbf{u}_j,\bar{z}_j)$  corresponding to pixel  $j\in \mathbf{I}_1$ , back-projected from the pixel with image coordinates  $\mathbf{u}_j\in \Omega_1$  ( $\Omega_1$  is the image domain for  $\mathbf{I}_1$ ) and its sensor (ground truth) depth  $\bar{z}_j\equiv \bar{\mathbf{Z}}_1[\mathbf{u}_j] -$  to its ground truth equivalent 3D point  $\bar{\mathbf{P}}_{j'} = \pi^{-1}(\bar{\mathbf{u}}_{j'},\bar{z}_{j'})$  back-projected from the true associated pixel with image coordinates  $\bar{\mathbf{u}}_{j'}\in \Omega_2$  ( $\Omega_2$  as the image domain for  $\mathbf{I}_2$ ) and sensor depth  $\bar{z}_{j'}\equiv \bar{\mathbf{Z}}_2[\bar{\mathbf{u}}_{j'}]$ .  $\pi^{-1}(\mathbf{u},z)$  stands for the inverse projection model. As the estimated scene flow  $\mathbf{T}^k$  might be affected by noise, we will denote as  $\mathbf{P}_{j'}^k = \mathbf{T}^k [\mathbf{u}_j]\mathbf{P}_j$  the transformation of  $\mathbf{P}_j$  to the local frame of  $\mathbf{I}_2$ , that in general will not coincide the true corresponding point  $\bar{\mathbf{P}}_{j'}$ .  $\mathbf{T}^k[\mathbf{u}_j]$  is composed of the rigid transformation coming from the camera motion  $\mathbf{T}_c^k$  and the one coming from the non-rigid surface deformations  $\mathbf{T}_d^k[\mathbf{u}_j]\in \mathrm{SE}(3)$ , so that  $\mathbf{T}^k[\mathbf{u}_j] = \mathbf{T}_c^k\mathbf{T}_d^k[\mathbf{u}_j]$ . If the scene is rigid  $\mathbf{P}_{j'}^k = \mathbf{T}_c^k\mathbf{P}_j^k$ , and if it is deforming  $\mathbf{P}_{j'}^k = \mathbf{T}_c^k\mathbf{T}_d^k[\mathbf{u}_j]\mathbf{P}_j^k$ .
Figure 4: Drunkard's Odometry architecture in detail.
Our iterative pose estimation model, shown in detail in Figure 4, is based on the foundations of the 3D flow estimation architecture of RAFT-3D [35], which does not have pose estimation capabilities at all. Firstly, a pose regression network encodes both color images  $\mathbf{I}_1$  and  $\mathbf{I}_2$  and outputs a initial pre-estimate for the camera motion  $\mathbf{T}_c^{\mathrm{pre}}$ . This rigid transformation is used for initialization, so that for any  $\mathbf{u}_j$  pixel of camera 1  $\mathbf{T}^{\mathrm{pre}}[\mathbf{u}_j] = \mathbf{T}_c^{\mathrm{pre}}$ . Additionally, we encode both images  $\mathbf{I}_1$  and  $\mathbf{I}_2$  in two feature maps, which we use to build a 4D correlation pyramid between the features of all pixel pairs at four different scales, each scale halving the resolution of the previous one. From  $\mathbf{T}^k$  and  $\bar{\mathbf{Z}}_1$  at each iteration  $k$ , we can obtain dense 2D pixel correspondences  $\mathbf{u}_j \rightarrow \mathbf{u}_{j'}$
$$
\mathbf {u} _ {j ^ {\prime}} ^ {k} = \pi (\pi^ {- 1} (\mathbf {u} _ {j}, \bar {z} _ {j}), \mathbf {T} ^ {k} [ \mathbf {u} _ {j} ]) ， \tag {1}
$$
where  $\pi (\mathbf{P},\mathbf{T}[\mathbf{u}])$  stands for the pinhole camera projection model. At the beginning of each iteration, these correspondences are used to sample the correlation features from the fixed 4D correlation pyramid and it is one of the inputs of the update block. The estimated optical flow  $\mathbf{O}^k\in \mathbb{R}^{w\times h\times 2}$  coming from these correspondences is also an input for the update block, being  $\mathbf{O}^k [\mathbf{u}_j] = \mathbf{u}_{j'}^k -\mathbf{u}_j$
The update block also takes as input an inverse depth residual obtained from the difference between the estimated inverse depth map  $\bar{\Lambda}_2^k\in \mathbb{R}^{w\times h}$  and the sensor inverse depth  $\bar{\Lambda}_2\in \mathbb{R}^{w\times h}$ ,  $\bar{\Lambda}_2[\mathbf{u}_{j'}^k] = 1 / \bar{\mathbf{z}}_2[\mathbf{u}_{j'}^k]$ ,  $\forall \mathbf{u}_{j'}^k\in \Omega_2$ . Values for  $\Lambda_2^k$  are interpolated from the grid defined by  $\lambda_{2,j'}^k = \Lambda_2^k [\mathbf{u}_{j'}^k ] = \mathbf{e}_3^\top \mathbf{P}_{j'}^k$ .  $\mathbf{e}_3 = (001)^\top$  acts as a selector of the third dimension of the 3D point.
A pair of context features that extracts semantic and contextual information from image 1 are also pre-rendered before entering the iterative process using a context encoder network and given to the update block. One context feature map is kept fixed during all iterations and another is used as initialization of the hidden state of the General Recurrent Unit (GRU) [69], which is at the heart of the update block (see the network fine-grained details in the supplementary material).
At each iteration  $k$ , the estimates for  $\mathbf{T}^k$  and  $\mathbf{T}_c^k$  are mapped to the Lie algebra with a logarithm map to result in a twist field  $\mathfrak{t}^k = \log_{\mathrm{SE}(3)}(\mathbf{T}^k)$  and  $\mathbf{t}_c^k = \log_{\mathrm{SE}(3)}(\mathbf{T}_c^k)$  before being given to the update block. The update operator outputs a set of updates for the optical flow  $\mathbf{O}^k$ , for the twist camera pose  $\mathbf{t}_c^k$  and for the inverse depth  $\Lambda_2^k$ , the hidden state of the recurrent network, a mask and a set of rigid-motion embeddings and confidence maps. These last two maps, next to the updated estimate of  $\Lambda_2^k$  and  $\mathbf{O}^k$ , and  $\mathbf{Z}_1$  are used by the least-squares optimizer block to update  $\mathbf{T}^k$  (see details in [35] for this update). Scene flow is updated as  $\mathbf{T}^{k + 1} = \mathbf{T}^k\exp_{\mathrm{SE}(3)}(\mathbf{t}^k)$ .
Internally the network works at  $1/8^{th}$  of the original resolution, and the estimated mask at each iteration of the update block is used to perform a convex upsampling to the original resolution of  $\mathbf{T}^k$  and intermediate updated 2D flow  $\mathbf{O}^{k,\mathrm{pre}} \in \mathbb{R}^{w \times h \times 2}$  by the update block.
The supervision comes from comparing the estimated optical flow  $\mathbf{O}^k$  obtained from  $\mathbf{T}^k$  and  $\bar{\mathbf{Z}}_1$  with Eq. 1 and the intermediate pre-estimated  $\mathbf{O}^{k,\mathrm{pre}}$  with the ground truth optical flow  $\bar{\mathbf{O}}\in \mathbb{R}^{w\times h\times 2}$ , the inverse depth error between  $\Lambda_{2}$  and  $\bar{\Lambda}_{2}$ , the relative camera pose error of  $\mathbf{T}_c$  and the initial guess pre-estimated by the pose network  $\mathbf{T}_c^{\mathrm{pre}}$  against  $\bar{\mathbf{T}}_c$ . The total loss results in
$$
\mathcal {L} = \mathcal {L} _ {\text {p o s e}} ^ {\text {p r e}} + \sum_ {k = 1} ^ {M} \gamma^ {M - k} \left(\mathcal {L} _ {\text {f l o w}} ^ {k} + \mathcal {L} _ {\text {d e p t h}} ^ {k} + \mathcal {L} _ {\text {p o s e}} ^ {k}\right) ， \tag {2}
$$
with  $\mathcal{L}_{\mathrm{flow}}^k = \sum_{\mathbf{u}_j}\left(\| \mathbf{O}^k -\bar{\mathbf{O}}\| _1 + w_1\| \mathbf{O}^{\mathrm{k,pre}} - \bar{\mathbf{O}}\| _1\right)$  being the optical flow loss term,  $\mathcal{L}_{\mathrm{depth}}^{k} =$ $w_{2}\sum_{\mathbf{u}_{j}}\| \Lambda_{2}^{k}[\mathbf{u}_{j}] - \bar{\Lambda}_{2}[\mathbf{u}_{j}]\|_{1}$  the inverse depth loss term, and  $\mathcal{L}_{\mathrm{pose}} = w_3\| \log_{\mathrm{SE(3)}}\left(\mathbf{T}_c\bar{\mathbf{T}}_c^{-1}\right)\| _1$  and  $\mathcal{L}_{\mathrm{pose}}^{\mathrm{pre}} = w_4\| \log_{\mathrm{SE(3)}}\left(\mathbf{T}_c^{\mathrm{pre}}\bar{\mathbf{T}}_c^{-1}\right)\| _1$  the relative camera pose loss terms.  $w_{l}$  stands for the relative weight of the  $l^{th}$  loss term and  $\gamma$  weights each loop.
In this section, we show the evaluation results of our Drunkard's Odometry against several relevant baselines in two non-rigid datasets: our synthetic Drunkard's and the real Hamlyn data [39, 30, 40, 41].
Drunkard's Setup. We trained our Drunkard's Odometry in all scenes of the Drunkard's Dataset except the test ones, with around  $90 - 10\%$  ratio for training and testing, respectively, of the difficulty level 1 with an input resolution of  $320 \times 320$ , batch size of 12, learning rate of  $10^{-4}$ , Adam optimizer [70], weight decay of  $10^{-5}$ , 12 iterations of the iterative block during training and test, hyperparameters  $w_{1} = 0.2$ ,  $w_{2} = 100$ ,  $w_{3} = 200$  and  $w_{4} = 6$ , and during 10 epochs ( $\sim 3$  days) on a single RTX Nvidia Titan. An ablation study available in the Supplementary Material was performed to obtain the hyperparameter values. Everything was trained from scratch except for the pose network encoder which was pre-trained on ImageNet [71].
Drunkard's Benchmark. We put to fight our Drunkard's Odometry in the Drunkard's Dataset benchmark against the gold-standard SfM pipeline COLMAP [72, 73], that searches for matches across all images in an offline manner using only the RGB channels of our images; the robust and accurate DROID-SLAM [34] that uses a combination of local-online and global-offline bundle adjustment refinements, trained also in virtual environments of buildings with optical flow and with a well-proved generalization capability between datasets, evaluated using the same RGB-D images as Drunkard's Odometry; and the frame-to-frame tracking designed for non-rigid endoscopic scenes Endo-Depth-and-Motion (EDaM) [9] that here uses the RGB plus the single-view estimated depth maps by a network trained self-supervisely on monocular images of KITTI [43]. The non-rigid SfM SD-DefSLAM [26] was tested but fails in the beginning of the sequences. Complex non-rigid optimization methods such as this one are unstable and tend to fail in difficult sequences with complicated 3D surfaces and abrupt camera trajectories like the ones in our Drunkard's Dataset.
Scene Method	Align-ment	Level 0	Level 1	Level 2	Level 3
frames [%]↑	RPE [cm]↓	RPE [°]↓	ATE [m]↓	frames [%]↑	RPE [cm]↓	RPE ATE [°]↓	RPE ATE [m]↓	frames [%]↑	RPE [cm]↓	RPE [°]↓	ATE [m]↓	frames [%]↑	RPE [cm]↓	RPE ATE [°]↓	ATE [m]↓
0	COLMAP [72]	Sim(3)	42	0.36	0.10	0.11	42	0.85	0.18	0.12	42	1.89	0.35	0.81	25	3.64	0.51	1.14
DROID-SLAM [34]	SE(3)	100	0.77	0.28	2.38	100	1.41	0.42	1.79	100	2.53	0.69	1.26	100	3.58	1.01	1.00
EDaM [9]	Sim(3)	100	1.83	1.21	1.49	100	1.82	1.37	1.83	100	2.05	1.72	1.95	100	2.66	2.27	2.01
EDaM w/ GT depth	SE(3)	100	2.05	1.13	2.01	100	2.28	1.28	1.78	100	2.89	1.17	2.24	100	3.57	2.15	2.42
Drunkard's Odometry	SE(3)	100	0.34	0.10	0.67	100	0.59	0.16	1.08	100	1.14	0.28	1.35	100	1.82	0.48	1.74
4	COLMAP [72]	Sim(3)	32	0.38	0.083	0.10	32	1.2	0.15	0.24	32	3.12	0.30	0.90	23	8.59	0.71	2.31
DROID-SLAM [34]	SE(3)	0	-	-	-	0	-	-	-	0	-	-	-	0	-	-	-
EDaM [9]	Sim(3)	100	5.50	2.16	4.85	100	5.27	2.27	4.81	100	5.39	2.56	4.79	100	5.88	2.96	4.90
EDaM w/ GT depth	SE(3)	100	6.01	2.02	7.58	100	5.99	2.14	7.26	100	6.31	2.40	7.94	30	6.81	2.90	2.66
Drunkard's Odometry	SE(3)	100	0.60	0.14	1.21	100	0.83	0.18	1.39	100	1.43	0.28	2.46	100	2.26	0.46	4.66
5	COLMAP [72]	Sim(3)	100	0.40	0.08	0.20	80	1.12	0.16	0.53	100	3.58	0.356	1.38	31	4.95	0.46	2.45
DROID-SLAM [34]	SE(3)	100	0.56	0.21	1.25	100	1.52	0.39	1.56	100	3.16	0.67	2.43	100	4.69	1.02	2.70
EDaM [9]	Sim(3)	100	3.05	1.98	2.82	100	3.13	2.11	2.73	100	3.57	2.46	2.99	100	4.12	2.98	2.86
EDaM w/ GT depth	SE(3)	100	4.63	1.95	4.00	100	4.65	2.07	2.86	100	5.21	2.43	4.19	100	6.01	2.91	4.03
Drunkard's Odometry	SE(3)	100	0.45	0.13	0.47	100	0.74	0.18	0.70	100	1.44	0.29	1.24	100	2.40	0.49	2.45