This paper has been accepted for publication at theIEEE Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, 2019. c IEEE

Events-to-Video: Bringing Modern Computer Vision to Event Cameras
Henri Rebecq †René Ranftl ‡Vladlen Koltun ‡Davide Scaramuzza †Abstract
Event cameras are novel sensors that report brightnesschanges in the form of asynchronous “events” instead ofintensity frames. They have significant advantages overconventional cameras: high temporal resolution, high dynamic range, and no motion blur. Since the output of event
cameras is fundamentally different from conventional cameras, it is commonly accepted that they require the development of specialized algorithms to accommodate the particular nature of events. In this work, we take a different view and propose to apply existing, mature computer
vision techniques to videos reconstructed from event data.
We propose a novel recurrent network to reconstruct videos
from a stream of events, and train it on a large amountof simulated event data. Our experiments show that ourapproach surpasses state-of-the-art reconstruction methods by a large margin (> 20%) in terms of image quality. We further apply off-the-shelf computer vision algorithms to videos reconstructed from event data on tasks
such as object classification and visual-inertial odometry,and show that this strategy consistently outperforms algorithms that were specifically designed for event data. We
believe that our approach opens the door to bringing theoutstanding properties of event cameras to an entirely new
range of tasks. A video of the experiments is available athttps://youtu.be/IdYrC4cUO0I

Y

E2VID

X

Time
Downstream applications

Object classification

Visual-inertial odometry

Off-the-shelf algorithm

Figure 1. Our network converts a spatio-temporal stream of events(left) into a high-quality video (right). This enables direct application of off-the-shelf computer vision algorithms such as object classification (Section 5.1) and visual-inertial odometry (Section 5.2), yielding state-of-the-art results with event data in eachcase. This figure shows an actual output sample from our method,
operating on a real event sequence from a publicly available
dataset [31].

blur, and provide measurements with a latency as low as
one microsecond. Event cameras thus provide a viable alternative, or complementary, sensor in conditions that arechallenging for conventional cameras.
However, since the output of an event camera is an asynchronous stream of events (a representation that is fundamentally different from natural images), existing computer
vision techniques cannot be directly applied to this data. As
a consequence, custom algorithms need to be specificallytailored to leverage event data. Such specialized algorithms
have demonstrated impressive performance in applications
ranging from low-level vision tasks, such as visual odometry [18, 40, 55, 39, 43], feature tracking [20, 54, 15] andoptical flow [5, 3, 49, 57, 48], to high-level tasks such as object classification [36, 21, 47] and gesture recognition [2].
While some works [9, 10, 5, 30, 18, 14] focused on exploiting the low latency of the sensor by processing the data
event-by-event, significant progress has been made by mapping a set of events into an image-like 2D representation
prior to processing. Examples are the integration of eventson the image plane [40, 27, 28] as well as time surfaces[21, 47, 57, 53, 57]. However, neither event images nor timesurfaces are natural images, meaning that much of the existing computer vision toolbox cannot be applied effectively.
Most importantly, deep networks trained on real image data

1. Introduction
Event cameras are bio-inspired vision sensors that work
radically differently from conventional cameras. Instead of
capturing intensity images at a fixed rate, event cameras
measure changes of intensity asynchronously at the timethey occur. This results in a stream of events, which encode
the time, location, and polarity (sign) of brightness changes(Fig. 2). Event cameras such as the Dynamic Vision Sensor(DVS) [25] possess outstanding properties when compared
to conventional cameras. They have a very high dynamic
range (140 dB versus 60 dB), do not suffer from motion† Dept. Informatics, Univ. of Zurich and Dept. Neuroinformatics,
Univ. of Zurich and ETH Zurich‡ Intel Labs

1

cannot be directly transferred to these representations.
In this paper, we propose to establish a bridge between
vision with event cameras and conventional computer vision. Specifically, we learn how to reconstruct natural
videos from a stream of events (“events-to-video”), i.e. welearn a mapping between a stream of events and a stream ofimages (Fig. 1). This allows us to apply off-the-shelf computer vision techniques to event cameras.
Our work differs from previous image reconstruction approaches [3, 32, 45] in two essential ways. First, instead ofembedding handcrafted smoothness priors into our reconstruction framework, we directly learn video reconstruction
from events using a large amount of simulated event data.
Second, instead of focusing mainly on the quality of the reconstructions, we build our approach with the goal of applying standard computer vision techniques to the reconstructions. To this end, we encourage the reconstructed images
to share the statistics of natural images through a perceptual
loss that operates on mid-level image features.
To further validate the quality of our approach, we use
our reconstructions to solve two popular problems with
event cameras: (i) object classification from a stream of
events, and (ii) visual-inertial odometry. We apply off-theshelf computer vision algorithms that were built to process
conventional images to the reconstructed videos for bothtasks. We show that this strategy outperforms state-of-theart methods that had been specifically designed for eventdata.
In summary, our contributions are:
• A novel recurrent network architecture to reconstructa video from a stream of events, which outperforms
the state-of-the-art in terms of image quality by a large
margin.
• We establish that the network can be trained from simulated event data and generalizes remarkably well toreal events.
• The application of our method to two problems with
event cameras: object classification and visual-inertial
odometry from event data. Our method outperforms
state-of-the-art algorithms designed specifically forevent data in both applications.
We believe that the most alluring characteristic of our
method is that it acts as a bridge between conventional cameras and event cameras, thus bringing the main stream ofcomputer vision research to event cameras: mature algorithms, modern deep network architectures, and weights
pretrained from large natural image datasets. We believethat our work will open the door to leveraging the benefitsof event cameras – high temporal resolution, high dynamic
range (Fig. 6), and no motion blur (Fig. 9) – to a broader
array of applications.

2. Related WorkEvents-to-video reconstruction is a popular topic in theevent camera literature. Early approaches did not reconstruct videos, but focused on the reconstruction of a single image from a large set of events collected by an event
camera moving through a static scene. These works exploit the fact that every event provides one equation relating the intensity gradient and optic flow through brightness
constancy [15]. Cook et al. [10] used bio-inspired, interconnected networks to simultaneously recover intensity images, optic flow, and angular velocity from an event camera performing small rotations. Kim et al. [17] developed
an Extended Kalman Filter to reconstruct a 2D panoramic
gradient image (later upgraded to a full intensity frame by
2D Poisson integration) from a rotating event camera, andlater extended it to a 3D scene and 6 degrees-of-freedom(6DOF) camera motion [18] (albeit in a static scene only).
Bardow et al. [3] proposed to estimate optic flow and intensity simultaneously from sliding windows of events througha variational energy minimization framework. They showed
the first video reconstruction framework from events that is
applicable to dynamic scenes. However, their energy minimization framework requires multiple hand-crafted regularizers, which can result in severe loss of detail in the reconstructions.
Recently, methods based on direct event integration haveemerged. These approaches do not rely on any assumption about the scene structure or motion dynamics, and cannaturally reconstruct videos at arbitrarily high framerates.
Munda et al. [32] cast intensity reconstruction as an energy
minimization problem defined on a manifold induced by theevent timestamps. They combined direct event integration
with total variation regularization and achieved real-time
performance on the GPU. Scheerlinck et al. [45] proposed
to filter the events with a high-pass filter prior to integration,and demonstrated video reconstruction results that are qualitatively comparable with [32] while being computationallymore efficient. While these approaches currently define thestate-of-the-art, both suffer from artifacts which are inherent to direct event integration. The reconstructions sufferfrom “bleeding edges” caused by the fact that the contrast
threshold (the minimum brightness change of a pixel to trigger an event) is neither constant nor uniform across the image plane. Additionally, pure integration of the events canin principle only recover intensity up to an unknown initial
image I0 , which causes “ghosting” effects where the traceof the initial image remains visible in the reconstructed images.
Barua et al. [4] proposed a learning-based approachto reconstruct intensity images from events. They used
K-SVD [1] on simulated data to learn a dictionary that maps
small patches of integrated events to an image gradient andused Poisson integration to recover the intensity image. In

ek−1
0

ek−1
i

standard
cameraoutput:event
cameraoutput:kek−1
N −1 e0εk−1

ekN −1
εk

Îk−2

Ek−1

Îk−1

Ek

Îk−3

A

Îk−2

A

Îk−4

t

Îk−3

Îk

Figure 2. Comparison of the output of a conventional camera andan event camera looking at a black disk on a rotating circle. Whilea conventional camera captures frames at a fixed rate, an event
camera transmits the brightness changes continuously in the formof a spiral of events in space-time (red: positive events, blue: negative events). Figure inspired by [30].

Figure 3. Overview of our approach. The event stream (depicted
as red/blue dots on the time axis) is split into windows εk with N
events in each. Each window is converted into a 3D event tensor
Ek and passed through the network together with the last K reconstructed images to generate a new image reconstruction Îk . In
this example, K = 3 and N = 7.

contrast to [4], we do not reconstruct individual intensity
images from small windows of events, but instead reconstruct a temporally consistent video from a long stream of
events (several seconds) using a recurrent network. Insteadof mapping event patches to a dictionary of image gradients,
we learn pixel-wise intensity estimation directly.
Despite the body of work on events-to-video reconstruction, further downstream vision applications based on thereconstructions have, to the best of our knowledge, neverbeen demonstrated prior to our work.

εk (see Fig. 3). The reconstruction function is implemented
by a recurrent convolutional neural network. We train the
network in supervised fashion, using a large quantity of simulated event sequences with corresponding ground-truth images. Because we process windows with a constant numberof events, the output framerate is proportional to the event
rate, making our approach fully data-driven. While ourmethod introduces some latency due to processing eventsin windows, it nonetheless captures the major advantagesof event cameras: our reconstructions have a high dynamic
range (Fig. 6) and are free of motion blur, even at highspeeds (Fig. 9).

3. Video Reconstruction ApproachAn event camera consists of independent pixels that respond to changes in the spatio-temporal brightness signal
L(x, t)1 and transmit the changes in the form of a stream ofasynchronous events (Fig. 2). For an ideal sensor, an event
ei = (ui , ti , pi ) is triggered at pixel ui = (xi , yi )T andtime ti when the brightness change since the last event at
the pixel reaches a threshold ±C, which can be fixed by theuser. However, C is in reality neither constant nor uniform
across the image plane. Instead, it strongly varies depending on various factors, such as the sign of the brightnesschange [14], the event rate (because of limited pixel bandwidth) [8], and the temperature [25]. Consequently, events
cannot by directly integrated to recover accurate intensity
images in practice.

3.1. Overview
Our goal is to translate a continuous stream of events
W×H
into a sequence of images {Îk }, where Îk ∈ [0, 1].
To achieve this, we partition the incoming stream of events
into sequential (non-overlapping) spatio-temporal windowsof events εk = {ei }, for i ∈ [0, N − 1], each containing afixed number N of events. For each new event sequence εk ,we generate a new image Îk by fusing the K previous reconstructed images {Îk−K , ..., Îk−1 } with the new events
1 Event cameras respond in fact to logarithmic brightness changes, i.e.
L = log E where E is the irradiance.

3.2. Event Representation
In order to be able to process the event stream using a CNN, we need to convert εk into a fixed-size tensor representation Ek . A natural choice is to encode theevents in a spatio-temporal voxel grid [58]. The duration∆T = tkN −1 − tk0 spanned by the events in εk is discretized
into B temporal bins. Every event distributes its polarity pi
to the two closest spatio-temporal voxels as follows:Xpi max(0, 1 − |tn − t∗i |),(1)
E(xl , ym , tn ) =xi =xlyi =ymwhere t∗i , B−1∆T (ti − t0 ) is the normalized event timestamp. We use N = 25,000 events per window and B = 10
temporal bins, unless specified otherwise.

3.3. Training Data
Our network requires training data, i.e. a large amountof event sequences with corresponding ground-truth image
sequences. Formally, if we let ES = {E0 , ..., ET −1 } bea sequence of event tensors, and IS = {I0 , ..., IT −1 } bethe corresponding sequence of images, we need to generatea large dataset of mappings ES ↔ IS . However, there
exists no such large-scale dataset with event data and corresponding ground-truth images. Furthermore, images acquired by a conventional camera would provide poor groundtruth in scenarios where event cameras excel, namely highdynamic range and high-speed scenes. For these reasons,
we propose to train the network on synthetic event data,and show subsequently that our network generalizes to real
event data in Section 4.
We use the event simulator ESIM [38], which allows
simulating a large amount of event data reliably. ESIM renders images along the camera trajectory at high framerate,and interpolates the brightness signal at each pixel to approximate the continuous intensity signal needed to simulate an event camera. Consequently, ground-truth images
I are readily available. We map MS-COCO images [26]to a 3D plane, and simulate the events triggered by random
camera motion within this simple 3D scene. Examples ofgenerated synthetic event sequences are presented in the appendix. We enrich the training data by simulating a different set of positive and negative contrast thresholds for eachsimulated scene (sampled according to a normal distribution
with mean 0.18 and standard deviation 0.03; these parameters were chosen based on empirical data). This prevents
the network from learning to simply integrate events, which
would work on noise-free, simulated data, but would generalize poorly to real event data (for which the assumption of
a fixed contrast threshold does not hold). The camera sensor size is set to 240 × 180 pixels (to match the resolutionof the DAVIS240C sensor [7] used in our evaluation). Using MS-COCO images allows capturing a much larger variety of scenes than is available in any existing event camera dataset. We generate 1,300 sequences of 2 seconds
each, which results in approximately 45 minutes of simulated event data. Note that the simulated sequences contain
only globally homographic motion (i.e. there is no independent motion in the simulated sequences). Nevertheless, ournetwork generalizes surprisingly well to scenes with arbitrary motions, as will be shown in Section 4.

3.4. Network Architecture and Training
The main module of our recurrent network is a UNet [42]architecture similar to the one introduced by Zhu et al. [57]in the context of optical flow estimation. The input tensor (obtained by concatenating Ek , Îk−K , ..., Îk−1 ), of size
(B + K)×H ×W , is passed through 4 strided convolution
layers (the number of output channels doubling each time),followed by two residual blocks [16] and four upsamplingtransposed convolution layers. The resulting activation is
convolved depthwise to obtain a final image reconstruction.
Following [57], we use skip connections between symmetric convolution layers. Additional details of the architecture
are provided in the appendix. On top of this basic module(labeled “A” in Fig. 3), we introduce a recurrent connection to propagate intensity information forward in time; inother words, the network does not need to reconstruct a newimage from scratch at every time step, but only to incre-

mentally update the previous reconstructions using the new
sequence of events. During training we unroll the network
for L steps. At test time, the preceding K reconstructed images are fed into the network (Fig. 3). We found that L = 8
and K = 3 provide a good trade-off between reconstruction
quality and training time.
Loss: We use the calibrated perceptual loss (LPIPS) [51],which passes the reconstructed image and the target image
through a VGG network [46] trained on ImageNet [44],and averages the distances between VGG features computed across multiple layers. By minimizing LPIPS, ournetwork effectively learns to endow the reconstructed images with natural statistics (i.e. with features close to thoseof natural
PL images). The total loss Lk is computed as
Lk = l=0 dL (Îk−l , Ik−l ), where dL denotes the LPIPS
distance.
Training Procedure: We split the synthetic sequences
into 1,270 training sequences and 30 validation sequences,and implement our network using PyTorch [35]. We use
ADAM [19] with an initial learning rate of 0.0001, subsequently decayed by a factor of 0.9 every 10 epochs. We use
a batch size of 16 and train for 40 epochs.

4. Evaluation
In this section, we present both quantitative and qualitative results on the fidelity of our reconstructions, and compare to recent methods [3, 32, 45]. We focus our evaluation
on real event data. An evaluation on synthetic data can befound in supplementary material.
We use event sequences from the Event Camera
Dataset [31]. These sequences were recorded using a
DAVIS240C sensor [7] moving in various environments. It
contains events as well as ground-truth grayscale frames at a
rate of 20 Hz. We remove the redundant sequences (e.g. thatwere captured in the same scene) and those for which theframe quality is poor, leaving seven sequences in total thatamount to 1,670 ground-truth frames. For each sequence,
we reconstruct a video from the events with our method andeach baseline. For each ground truth frame, we query the reconstructed image with the closest timestamp to the groundtruth (tolerance of ±1 ms).
Each reconstruction is then compared to the corresponding ground-truth frame according to several quality metrics.
We equalize the histograms of every ground-truth frame
and reconstructed frame prior to computing the error metrics (this way the intensity values lie in the same intensity
range and are thus comparable). Note that the camera speed
gradually increases in each sequence, leading to significant
motion blur on the ground-truth frames towards the end of
the sequences; we therefore exclude these fast sections in(a) Scene overview(b) Events(c) HF(d) MR(e) Ours(f) Ground truth

Figure 4. Comparison of our method with MR and HF on sequences from [31]. Our network reconstruct fine details well (textures in thefirst row) compared to the competing methods, while avoiding their artifacts (e.g. the “bleeding edges” in the third row).

our quantitative evaluation. We also omit the first few seconds from each sequence, which leaves enough time for thebaseline methods that are based on event integration to converge. Note that this works in favor of the baselines, as
our method converges almost immediately (the initialization phase is analyzed in the supplementary material).
We compare our approach against several state-of-theart methods: [3] (which we denote as SOFIE for “Simultaneous Optic Flow and Intensity Estimation”), [45] (HF for“High-pass Filter”), and [32] (MR for “Manifold Regularization”). For HF and MR, we used the code that was provided by the authors and manually tuned the parameters on
the evaluated sequences to get the best results possible. For
HF, we also applied a bilateral filter to the reconstructed images (with filter size d = 5 and σ = 25) in order to remove
high-frequency noise, which improves the results of HF in
all metrics. For SOFIE, we report qualitative results insteadof quantitative results since we were not able to obtain satisfying reconstructions on our datasets using the code provided by the authors. We report three image quality metrics: mean squared error (MSE; lower is better), structuralsimilarity (SSIM; higher is better) [50], and the calibrated
perceptual loss (LPIPS; lower is better) [51].
Results and Discussion: The main quantitative results arepresented in Table 1, and are supported by qualitative resultsin Figs. 4 and 5. Additional results are available in the supplementary material. We also encourage the reader to watch
the supplementary video, which conveys these results in abetter form than still images can.
On all datasets, our reconstruction method outperforms
the state-of-the-art by a large margin, with an average 21%
increase in SSIM and a 23% decrease in LPIPS. Quali-(a) Events(b) SOFIE(c) HF(d) MR(e) Ours

Figure 5. Qualitative comparison on the dataset introduced by [3].
Our method produces cleaner and more detailed results.

tatively, our method reconstructs small details remarkably
well compared to the baselines (see the boxes in the firstrow of Fig. 4, for example). Furthermore, our method does
not suffer from “ghosting” or “bleeding edges” artifacts thatare present in other methods (particularly visible in the third
row of Fig. 4). These artifacts result from (i) incorrectly estimated contrast thresholds and (ii) the fact that these methods can only estimate the image intensity up to some unknown initial intensity I0 , whose ghost can remain visible.
We also compare our method to HF, MR, and SOFIE qualitatively using datasets and image reconstructions directly
provided by the authors of [3], in Fig. 5. Once again, ournetwork generates higher quality reconstructions, with finer
details and less noise. Finally, we show that our network isable to leverage the outstanding properties of events to reconstruct images in low light (Fig. 6) and during high speed
motions (Fig. 9), two scenarios in which conventional cameras fail.
Limitations: Our method introduces some latency due tothe fact that we process events in windows as opposed toMSE
Dataset

SSIM

LPIPS

HF

MR

Ours

HF

MR

Ours

HF

MR

Ours

dynamic 6dof
boxes 6dof
poster 6dof
shapes 6dof
office zigzag
slider depth
calibration

0.10
0.09
0.06
0.11
0.09
0.08
0.07

0.11
0.07
0.05
0.14
0.06
0.08
0.06

0.08
0.04
0.04
0.10
0.05
0.06
0.04

0.39
0.45
0.52
0.34
0.36
0.48
0.41

0.44
0.47
0.55
0.43
0.43
0.51
0.41

0.50
0.63
0.68
0.44
0.50
0.61
0.52

0.53
0.51
0.44
0.63
0.54
0.50
0.55

0.53
0.54
0.50
0.64
0.55
0.55
0.57

0.43
0.36
0.32
0.53
0.44
0.42
0.47

Mean

0.09

0.08

0.06

0.42

0.46

0.56

0.53

0.55

0.42

Table 1. Comparison to state-of-the-art image reconstruction methods on the Event Camera Dataset [31]. Our approach outperforms priorsuch methods on all datasets by a large margin, with an average 21% increase in structural similarity (SSIM) and a 23% decrease inperceptual distance (LPIPS) compared to the best prior methods, respectively MR [32] and HF [45].

tion with events and inertial measurements (Section 5.2).

5.1. Object Classification(a) VI Sensor(b) Events(c) Our reconstruction

Figure 6. A high-dynamic-range reconstruction from an event
camera mounted on a car driving at night [56]. Because of low
light, the conventional camera image is severely degraded (a). In
contrast, the events (b) capture the whole dynamic range of thescene, which our method successfully converts into an image (c),recovering details that are lost in the conventional frame.

(a)

(b)

(c)

(d)

Figure 7. An example failure case on a sequence from the MVSEC
dataset [56]. The sun (top left of (a)) generates noisy events whichcause our network to make a local reconstruction error (b), whichgets amplified in subsequent reconstructions (c,d).

event-per-event [45], ranging from 1 ms to 200 ms depending on the event rate and value of N (details in the appendix). Also, in some cases (extreme electronic noise in
the events not modelled in simulation), our method can failto reconstruct correctly some parts of the images, and may
propagate the error to the next reconstructions (Fig. 7).

5. Downstream Applications
In this section, we demonstrate the potential of our
method as a bridge between conventional computer visionand vision with event cameras, for both low-level and highlevel tasks. First, we focus on object classification fromevents (Section 5.1) and then turn to camera pose estima-

Pattern recognition from event data is an active research
topic.2 . While one line of work focuses on spiking neuralarchitectures (SNNs) to recognize patterns from a streamof events with minimal latency (H-FIRST [34]), conventional machine learning techniques combined with novel
event representations such as time surfaces (HOTS [21])have shown the most promising results so far. Recently,
HATS [47] addressed the problem of object classificationfrom a stream of events. They proposed several modifications to HOTS, and achieved large improvements in classification accuracy, outperforming all prior approaches by alarge margin.
We propose an alternative approach to object classification based on a stream of events. Instead of using handcrafted event representations, we directly train a classification network on images reconstructed from events.
We compare our approach against several recent methods: HOTS, and the state-of-the-art HATS, using thedatasets and metric (classification accuracy) used in the
HATS paper. The N-MNIST (Neuromorphic-MNIST) and
N-Caltech101 datasets [33] are event-based versions of the
MNIST [22] and Caltech101 [12] datasets. To convert theimages to event sequences, an event camera was placed ona motor, and automatically moved while pointing at images
from MNIST (respectively Caltech101) that were projected
onto a white wall. The N-CARS dataset [47] proposes abinary classification task: deciding whether a car is visible
or not using a 100 ms sequence of events. Fig. 8 shows asample event sequence from each of the three datasets.
Our approach follows the same methodology for eachdataset. First, for each event sequence in the training
set, we use our network to reconstruct an image from the2 A list of related works can be found at: https://github.com/
uzh-rpg/event-based_vision_resources

(a) DAVIS frame(a) N-MNIST(b) N-CARS(c) N-Caltech101

Figure 8. Samples from each dataset used in the evaluation of our
object classification approach based on events (Section 5.1). Top:
preview of the event sequence. Bottom: our image reconstruction.

HOTS
HATS/linear SVM
HATS/ResNet-18
Ours (transfer learning)
Ours (fine-tuned)

N-MNIST

N-CARS

N-Caltech101

0.808
0.991
n.a.
0.807
0.983

0.624
0.902
0.904
n.a.
0.910

0.210
0.642
0.700
0.821
0.866

Table 2. Classification accuracy compared to recent approaches,
including HATS [47], the state-of-the-art.

events (Fig. 8, bottom row). Then, we train an off-the-shelf
CNN for object classification using the reconstructed images from the training set. For N-MNIST, we use a simple
CNN (details in the supplement) and train it from scratch.
For N-Caltech101 and N-CARS, we use ResNet-18 [16],initialized with weights pretrained on ImageNet [44], andfine-tune the network for the dataset at hand. Once trained,
we evaluate each network on the test set (images reconstructed from the events in the test set) and report the classification accuracy. Furthermore, we perform a transfer
learning experiment for the N-MNIST and N-Caltech101
datasets (for which corresponding images are available forevery event sequence): we train the CNN on the conventional image datasets, and evaluate the network directly on
images reconstructed from events without fine-tuning.
For the baselines, we report directly the accuracy provided in [47]. To make the comparison with HATS as fair as
possible, we also provide results of classifying HATS features with a ResNet-18 network (instead of the linear SVM
used originally). The results are presented in Table 2, where
the datasets are presented in increasing order of difficultyfrom left to right. Despite the simplicity of our approach, it
outperforms all baselines, and the gap between our method
and the state-of-the-art increases as the datasets get moredifficult. While we perform slightly worse than HATS on NMNIST (98.3% versus 99.1%), this can be attributed to thesynthetic nature of N-MNIST, for which our approach does
not bring substantial advantages compared to a hand-craftedfeature representation such as HATS. Note that, in contrast
to HATS, we did not perform hyperparameter tuning. On
N-CARS (binary classification task with natural event data),(b) Our reconstruction

Figure 9. Comparison of DAVIS frames and reconstructed frames
on a high-speed portion of the ‘dynamic 6dof’ sequence. Our reconstructions from events do not suffer from motion blur, whichleads to increased pose estimation accuracy (Table 3).

our method performs better, though the improvement is minor (91% versus 90.4% for HATS). However, N-CARS isalmost saturated in terms of accuracy.
On N-Caltech101 (the most challenging dataset, requiring classification of natural event data into 101 object
classes), our method truly shines, outperforming HATS bya large margin (86.6% versus 70.0%). This significant gap
can be explained by the fact that our approach leverages
decades of computer vision research and datasets. Lifting
the event stream into the image domain with our events-tovideo approach allows us to use a mature CNN architecture
pretrained on existing large, labeled datasets, thus leveraging powerful hierarchical features learned on a large amountof image data – something that is not possible with event
data, for which labeled datasets are scarce. Finally, and perhaps even more strikingly, we point out that our approach,in the pure transfer learning setup (i.e. feeding images reconstructed from events to a network trained on real imagedata) performs better than all other methods, while not using
the event sequences from the training set. To the best of ourknowledge, this is the first time that direct transfer learning
between image data and event data has been achieved.
We also point out that our approach is real-time capable. On N-Caltech101, end-to-end classification takes lessthan 10 ms (sequence reconstruction: ≤ 8 ms, object classification: ≤ 2 ms) on an NVIDIA RTX 2080 Ti GPU. Moredetails about performance can be found in the appendix.

5.2. Visual-Inertial Odometry
The task of Visual-inertial odometry (VIO) is to recover
the 6-degrees-of-freedom (6-DOF) pose of a camera froma set of visual measurements (images or events) and inertial measurements from an inertial measurement unit (IMU)
that is rigidly attached to the camera. Because of its importance in augmented/virtual reality and mobile robotics, VIO
has been extensively studied in the last decade and is relatively mature today [29, 23, 6, 13, 37]. Yet systems based onconventional cameras fail in challenging conditions such as
high-speed motions or high-dynamic-range environments.
This has recently motivated the development of VIO systems with event data (EVIO) [55, 39, 43].

Inputs

Ours U.SLAM U.SLAM
E+I
E+I
E+F+I

HF
E+I

MR
E+I

VINS-Mono
F+I

shapes translation
poster translation
boxes translation
dynamic translation
shapes 6dof
poster 6dof
boxes 6dof
dynamic 6dof
hdr boxes

0.18
0.05
0.15
0.08
1.09
0.12
0.62
0.15
0.34

0.32
0.09
0.81
0.23
0.09
0.20
0.41
0.27
0.44

0.17
0.06
0.26
0.09
0.06
0.22
0.34
0.11
0.37

failed
0.49
0.70
0.58
failed0.45
1.71
failed
0.64

2.00
0.15
0.45
0.17
3.00
0.17
1.17
0.55
0.66

0.93
failed
0.22
0.13
1.99
1.99
0.94
0.76
0.32

Mean
Median

0.31
0.15

0.32
0.27

0.19
0.17

0.76
0.61

0.92
0.55

0.91
0.84

Table 3. Mean translation error (in meters) on the sequencesfrom [31]. Our method outperforms all other methods that use
events and IMU, including UltimateSLAM (E+I). Surprisingly, iteven performs on par with UltimateSLAM (E+F+I), while not using additional frames. Methods for which the mean translation
error exceeds 5 m are marked as “failed”.

The state-of-the-art EVIO system, UltimateSLAM [43],operates by independently tracking visual features frompseudo-images reconstructed from events and optional images from a conventional camera, and fusing the tracks withinertial measurements using an existing optimization backend [23]. Here, we go one step further and directly apply an off-the-shelf VIO system (specifically, VINS-Mono[37], which is state-of-the-art [11]) to videos reconstructedfrom events using either our approach, MR, or HF, and evaluate against UltimateSLAM. As is standard [55, 39, 43],
we use sequences from the Event Camera Dataset [31],which contain events, frames, and IMU measurements froma DAVIS240C [7] sensor. Each sequence is 60 seconds
long, and contains data from a hand-held event camera undergoing a variety of motions in several environments. All
sequences feature extremely fast motions (angular velocity
up to 880 ◦ /s and linear velocity up to 3.5 m/s), which leads
to severe motion blur on the frames (Fig. 9). We compare
our approach against the two operating modes of UltimateSLAM: UltimateSLAM (E+I) which uses only events and
IMU, and UltimateSLAM (E+F+I) that uses the events, the
IMU, and additional frames. We run a publicly available
VIO evaluation toolbox [52] on raw trajectories provided
by the authors of UltimateSLAM, which ensures that the
trajectories estimated by all methods are evaluated in theexact same manner. For completeness, we also report results from running VINS-Mono directly on the frames fromthe DAVIS sensor.
Table 3 presents the mean translation error of eachmethod, for all datasets (additional results are presented
in the supplement). First, we note that our method performs better than UltimateSLAM (E+I) on all sequences,with the exception of the ‘shapes 6dof’ sequence. This sequence features a few synthetic shapes with very few features (≤ 10), which cause VINS-Mono to not properly initialize, leading to high error (note that this is a problem with

VINS-Mono and not our image reconstructions). Overall,
the median error of our method is 0.15 m, which is almost
twice smaller than UltimateSLAM (E+I) (0.27 m), whichuses the exact same data. Indeed, while UltimateSLAM(E+I) uses coarse pseudo-images created from a single,
small window of events, our network is able to reconstruct
images with finer details, and higher temporal consistency– both of which lead to better feature tracks, and thus better pose estimates. Even more strikingly, our approach performs on par with UltimateSLAM (E+F+I), while the latter
requires additional frames which we do not need. The median error of both methods is comparable (0.15 m for ours
versus 0.17 m for UltimateSLAM (E+F+I)).
Finally, we point out that running the same VIO (VINSMono) on competing image reconstructions (MR and HF)
leads to significantly larger tracking errors (e.g. median error three times as large for MR), which further highlights
the superiority of our image reconstructions for downstream
vision applications. We acknowledge that our approach is
not as fast as UltimateSLAM. Since the main difference
between both approaches is how they convert events into
“image-like” representations, a rough estimate of the performance gap can be obtained by comparing the time ittakes for each method to synthesize a new image: UltimateSLAM takes about 1 ms on a CPU, in comparison to≤ 4 ms on a high-end GPU for our method. Nevertheless, our events-to-video network allows harnessing the outstanding properties of events for VIO, reaching even higher
accuracy than state-of-the-art EVIO designed specificallyfor event data.

6. Conclusion
We presented a novel events-to-video reconstruction
framework based on a recurrent convolutional network
trained on simulated event data. In addition to outperforming state-of-the-art reconstruction methods on real event
data by a large margin (> 20% improvement), we showed
the applicability of our method as a bridge between conventional cameras and event cameras on two vision applications, namely object classification from events and visualinertial odometry. For each of these tasks, we applied an
off-the-shelf computer vision algorithm to videos reconstructed from events by our network, and showed that theresult outperforms state-of-the-art algorithms tailored forevent data in each case. This validates that our approachallows to readily apply decades of computer vision research
to event cameras: mature algorithms, modern deep architectures, and weights pretrained from large image datasets.

Acknowledgements
This work was supported by the the Swiss National
Center of Competence Research Robotics (NCCR) and the
SNSF-ERC Starting Grant.

A. Architecture Details
Our reconstruction network is presented in detail in
Fig. 10. It is essentially a UNet architecture [42], composedof multiple convolutional layers. Four encoder layers (blue)
are followed by two residual blocks (yellow), four decoder
layers (red), and a final image prediction layer. In addition, symmetric skip connections are used. The encoders
are strided convolutional layers (stride of 2), with a kernel
size of 5. The number of output channels of the first encoder layer (↓conv1) is 64, and is doubled for every subsequent encoder layer, i.e. the sequence of output channels
is (64, 128, 256, 512). Both residual blocks have 512 hidden layers, and a kernel size of 3. Batch normalization isused within the residual blocks (applied before the activation). The decoders are transposed convolution layers, witha kernel size of 5. The number of output channels of thedecoders starts at 256 (↑conv1), and is divided by two forevery subsequent decoder, i.e. the sequence of output channels is (256, 128, 64, 32)). The kernel size for the residual
blocks is 3. ReLU is used as activation everywhere, except
for the last image prediction layer, where a sigmoid activation is used instead. The skip connections are based onconcatenation.

B. Initialization Phase
By analyzing the initialization phase (i.e. when only few
events have been triggered yet) in detail we gain interesting
insight into how our network operates. We see significantly
different behaviour when compared to prior approaches thatare based on direct event integration. Fig. 11 compares image reconstructions from our approach, HF, and MR during
the initialization phase. We specifically examine the interval from 0 s to 0.5 s after the event cameras has been started.
HF and MR, which rely on event integration, can only
recover the intensity up to the initial (unknown) image I0(i.e. they can only recover Î ≈ I − I0 ), which results inan “edge” image which does not capture the appearance ofthe scene correctly. In contrast, our method successfully
leverages deep priors to reconstruct the scene despite thelow number of events that is available.
By contrast, our method is able to leverage deep priors on
what a scene might look like (learned, first, from the large
amount of simulated event data, and second, embedded in
the perceptual loss used (LPIPS)) to reconstruct the scene
reasonably well even with a low number of events.

C. Why Use Synthetic Training Data?
Here, we expand on the reasons that motivated us to train
our reconstruction network using synthetic event data. First,
simulation allows to capture a large variety of scenes andmotions at very little cost. Second, a conventional camera(even a high quality one) would provide poor ground truthin high-speed conditions (motion blur) and HDR scenes,
which are the conditions in which event sensors excel; bycontrast, synthetic data does not suffer from these issues.
Last but not least, simulation allows to randomize the contrast thresholds of the event sensor, which increases the ability of the network to generalize to different sensor configurations (contrast sensitivity). To illustrate this last point, weshow in Fig. 12 (left) what happens when training the network on real event data from an event camera (specifically,
the sequences from the Event Camera Dataset [31] alreadypresented in the main paper, which were recorded with a
DAVIS240C sensor), and evaluating the trained network on
data coming from a different event sensor (specifically, the‘outdoors day1‘ sequence from the MVSEC dataset [56],which was recorded with a mDAVIS346 sensor): the reconstruction suffers from many artefacts. This can be explained by the fact that the events from the mDAVIS346
sensor have statistics that are quite different from the training events (DAVIS240C): the set of contrast thresholds arelikely quite different between both sensors, and the illumination conditions are also different (outdoor lighting for the
MVSEC dataset versus indoor lighting for the training eventdata). By contrast, the network trained on simulated event
data (Fig. 12, right) generalizes well to the event data fromthe mDAVIS346, producing a visually pleasing image reconstruction.

D. Latency and Performance Considerations
D.1. Latency
Our method operates on windows containing a fixed
number of events, which incurs some latency compared toapproaches that operate in an event-by-event fashion. The
amount of latency depends on the duration of the event windows. Therefore, it varies through time, depending on theevent rate. Since we process events in windows with a constant number of events, our method is data-driven, whichmeans that the latency diminishes when the event rate increases, i.e. when something of interest occurs in the scene.
Fig. 13 shows the distribution of the latency (in other words,
the durations of the event windows) for the sequences usedin our qualitative evaluation (Section 4 in the main paper):the latency is in the range of 3 ms to 300 ms, with a median value of 26 ms. We point out that while in this paper
we chose to process windows with a fixed number of events,
our methodology could also be applied to windows of a constant duration (which would require, however, to retrain the
network to operate with such fixed-length windows), yielding a constant, predictable latency, but at the cost of losing
the adaptive output framerate.

input↓conv1↓conv2↓conv3↓conv4res1

res2↑conv1↑conv2↑conv3↑conv4(B+K) x H x W

predoutput
1xHxWFigure 10. Detailed architecture of our reconstruction network. It is a UNet architecture [42], composed of multiple convolutional layers.
Four encoder layers (blue) are followed by two residual blocks (yellow), four decoder layers (red), and a final image prediction layer. In
addition, symmetric skip connections are used. More details about each layer are provided in the text.

(a) HF(b) MR(c) Ours(d) Ground truth

Figure 11. Analysis of the initialization phase (reconstruction from few events). This figure shows image reconstructions from each method,
0.5 seconds after the sensor was started. HF [45] and MR [32], which are based on event integration, cannot recover the intensity correctly,
but only the intensity difference , resulting in “edge ” images (first and second row), or severe “ghosting” effects (third row, where the traceof the dartboard is clearly visible). In contrast, our network successfully reconstructs most of the scene accurately, even with a low numberof events.

Figure 12. Reconstruction from (i) a network trained only on real
event data from the DAVIS240C sensor (left), and (ii) a network
trained only on simulated event data (right). This sequence is from
the MVSEC dataset, and was recorded with a mDAVIS346 sensor.

D.2. Performance Analysis
In this section, we analyze the performance of our
method, and compare it against HF [45] and MR [32]. Due

to fundamental differences in the way each of these methods process event data, it is difficult to provide a direct
and fair performance comparison between these three methods. HF processes the event stream in an event-by-event
fashion, providing (in theory) a new image reconstruction
with every incoming event. However, the raw image reconstructions from HF need to be filtered (for example, using
a bilateral filter) to obtain results with reasonable quality.
While MR can in principle also operate in an event-by-event
fashion, its best quality results are obtained when it processes small batches of events (in our experiments, we used
NM R = 1,000 events per batch), thus limiting the output
framerate. Our method, by contrast, processes the event
stream in large batches of events (we used N = 25,000
events per batch), thus also limiting the output framerate(i.e. increasing the latency, as analyzed in Fig. 13). In Ta-calibration
slider_depth
office_zigzag
shapes_6dof
poster_6dofboxes_6dof
dynamic_6dof
0

20

40

60

80
Latency (ms)

100

120

140

Figure 13. Distribution of latency for each dataset used in our quantitative evaluation.

ble 4, we report the mean event processing rate (i.e. the total
time it takes to process a dataset divided by the number of
events in the dataset) for each method. As a complementary performance measure, we also report the mean “frame
synthesis time”, which we define as follows:• for our method, it is the time it takes to process N
events.
• For MR, it is the time it takes to process NM R events.
• For HF, it is in theory the time it takes to process a single event (since every new event triggers a new reconstruction), plus the image filtering time. However, thetime to process a single event is negligible compared to
the filtering time (multiple orders of magnitude less),hence we report only the filtering time. As describedin the main text, we used a bilateral filter with filter
size d = 5 and σ = 25).
We ran our method and MR on an NVIDIA GeForce RTX
2080 Ti GPU, and HF on an Intel Core i9-9900K @ 3.60
GHz CPU.

HF
MR
Ours

Event rate (Mev/s)

Frame synthesis time (ms)

14.30
1.19
7.94

0.75 (filter)
0.84
3.15

Table 4. Performance comparison between our method, HF, andMR.

Discussion We point out that our method synthesizes
fewer images per second than MR and HF, hence the numbers in Table 4 should be interpreted carefully. That being
said, our method is fairly competitive in terms of performance, and, importantly, can easily run in real-time, whileproviding state of the art reconstructions in terms of quality.

E. Additional Results
E.1. Video
We strongly encourage the reader to view the supplemental video, which contains:• Video reconstructions from our method on various
event datasets, with a visual comparison to several
state of the art methods.
• Video of the VINS-Mono visual-inertial odometry algorithm [37] running on a video reconstruction fromevents.
• Qualitative results on two additional downstream applications that were not presented in the main paper:
object detection (based on YOLOv3 [41]), and monocular depth prediction (based on MegaDepth [24]). We
point out that neither of these tasks have ever beenshown with event data before this work.

E.2. Results on Synthetic Event Data
We show a quantitative comparison of the reconstruction
quality of our method as well as MR and HF on synthetic
event sequences in Table 5. We present qualitative reconstruction results on this dataset in Fig. 14. All methods perform better on synthetic data than real data. This is expected
because simulated events are free of noise. Nonetheless,the performance gap between our method and the state of
the art is preserved, and even slightly increases (24% improvement in SSIM, 56% decrease in LPIPS). We note that
perfect reconstruction, even on noise-free event streams is
not possible, since image reconstruction from events is only
possibly up the the quantization limit imposed by the contrast threshold of the event camera.

E.3. Additional Qualitative Results on Real Data
Fig. 15 shows qualitative results on sequences from the
Event Camera Dataset [31] (which we used for our quantitative evaluation). Fig 16 shows qualitative results on theMSE
Dataset

SSIM

LPIPS

HF

MR

Ours

HF

MR

Ours

HF

MR

Ours

synthetic 0
synthetic 1
synthetic 2
synthetic 3
synthetic 4
synthetic 5
synthetic 6

0.08
0.15
0.07
0.07
0.08
0.08
0.07

0.06
0.14
0.08
0.05
0.06
0.08
0.04

0.02
0.06
0.02
0.04
0.02
0.02
0.03

0.54
0.38
0.60
0.57
0.62
0.50
0.56

0.61
0.45
0.68
0.66
0.67
0.61
0.65

0.83
0.60
0.82
0.75
0.85
0.74
0.77

0.49
0.54
0.42
0.45
0.41
0.53
0.44

0.47
0.56
0.42
0.43
0.42
0.54
0.48

0.26
0.37
0.26
0.33
0.25
0.36
0.30

Mean

0.09

0.07

0.03

0.54

0.62

0.77

0.47

0.47

0.30

Table 5. Comparison of image quality with respect to state of the art on synthetic event sequences.

sequences introduced by Bardow et al. [3]. Figs. 17 and 18
present HDR reconstruction results on sequences from the
MVSEC dataset [56]. Further results are shown in the supplementary video which conveys these results in a better
form than still images.

F. Object Classification
Below we detail the exact modalities of our reconstruction method for each of the dataset which we used for ourevaluation of object classification (Section 5.1 in the paper),as well as the specific architectures used and training modalities.
N-MNIST. To reconstruct images with our networks, weused an event window of N = 1,000 events. We passed
every event sequence into our network, resulting in a video,from which we keep the final image as input for the classification network. To match the images from the original
MNIST dataset, we additionally binarize the reconstructed
image (whose values lie in [0, 1]) with a threshold of 0.5.
The train and test images were normalized so that the meanvalue of each image is 0.1307 and the variance 0.3081.
We used the official train and test split provided in the MNNIST dataset. As there is no standard state of the art architecture for MNIST, we used a simple CNN architecture
as our classification network, composed of the following
blocks:• 2D convolution (stride: 5, output channels: 32) + ReLU
• 2D convolution (stride: 5, output channels: 64) + ReLU
• 2D max pooling (size: 2) + Dropout• Fully connected layer (output size: 128 neurons) + ReLU
• Fully connected layer (output size: 10 neurons)

We used the cross entropy loss, and trained the networkfor 15 epochs using the ADAM optimizer, with a learning
rate of 0.001.
N-CARS. We used windows of events with a fixed temporal size of 20 ms, and used the last reconstructed image
from the video as input to the classification network. We

used the official train and test split provided by the N-CARS
dataset. We used a ResNet18 [16] architecture (with an additional fully connected final layer with 2 output neurons),
initialized with weights pretrained on ImageNet [44], andfine-tuned the network using the reconstructed images fromthe training set for 20 epochs, using SGD with a learning
rate of 0.001 (decayed by factor of 0.1 every 7 epochs), andmomentum of 0.1.
N-Caltech101. For image reconstruction, we used windows
of N = 10,000, events and used the last reconstructed image as input to the classification network. Since there is no
official train and test split for the N-Caltech101 dataset, we
split the dataset randomly into two third training sequences(5,863 sequences) and one third testing sequences (2,396
sequences), following the methodology used by HATS [47].
The train and test images were converted to 3-channelgrayscale images (i.e. the three channels are the same), andnormalized so that the mean value of each image is 0.485
and the variance 0.229. We also performed data augmentation at train time (random horizontal flips, and random
crop of size 224). At test time, we resized all the images to
256×256 and cropped the image around the center with asize of 224. We used a ResNet18 architecture (with an additional fully-connected final layer with 101 output neurons),
initialized with weights pretrained on ImageNet, and finetuned the network using the reconstructed images from thetraining set for 25 epochs using SGD with an initial learning
rate of 0.001 (decayed by a factor of 0.1 every 7 epochs) andmomentum of 0.1. Fig. 19 shows additional reconstruction
examples from the N-Caltech101 dataset.

G. Visual-Inertial Odometry
Figs. 20, 21 and 22 provide additional results on thevisual-inertial odometry experiments presented in the main
paper. Specifically, they provide, for each sequence used inour evaluation, the evolution of the mean translation androtation error as a function of the travelled distance forour approach, UltimateSLAM (E+I), and UltimateSLAM(E+F+I).

(a) Events(b) HF(c) MR(d) Ours(e) Ground truth

Figure 14. Qualitative comparison of our reconstruction method with HF [45] and MR [32] on synthetic sequences from the validation set.
Note our method is able to reconstruct fine details such as the bear’s fur (last row), which competing methods are not able to preserve.

(a) Scene Preview(b) Events(c) HF(d) MR(e) Ours(f) Ground truth

Figure 15. Qualitative comparison of our reconstruction method with two recent competing approaches, MR [32] and HF [45], on sequencesfrom [31], which contain ground truth frames from a DAVIS240C sensor. Our method successfully reconstructs fine details (textures inthe second and third row) compared to other methods, while avoiding ghosting effects (particulary visible in the shapes sequences on the
fourth row).

(a) Events(b) SOFIE [3](c) HF [45](d) MR [32](e) Ours

Figure 16. Qualitative comparison of our reconstruction method with various competing approaches. We used the datasets from [3]. The
dataset does not contain ground truth images, thus only a qualitative comparison is possible. For SOFIE and MR, we used images provided
by the authors, for which the parameters were tuned for each dataset. For HF, we ran the code provided by the authors, manually tuned theparameters on these datasets to achieve the best visual quality, and additionally applied a bilateral filter to clean the high frequency noisepresent in the original reconstructions.

(a) Events(b) VI sensor frame(c) Our reconstruction

Figure 17. Example HDR reconstructions on the MVSEC automotive dataset [56]. The standard frames were recorded with a high-quality
VI sensor with auto-exposure activated. Because the camera is facing directly the sun, the standard frames (b) are either under- or overexposed since the limited dynamic range of the standard sensor cannot cope with the high dynamic range of the scene. By contrast, theevents (a) capture the whole dynamic range of the scene, which our method successfully reconstructs to high dynamic range images (c),allow to discover details that were not visible in the standard frames.

(a) Events(b) VI sensor frame(c) Our reconstruction

Figure 18. Example HDR reconstructions on the MVSEC automotive dataset [56] at night. The standard frames were recorded with ahigh-quality VI sensor with auto-exposure activated. Because of low light during the night, the standard frames (b) are severely degraded.
By contrast, the events (a) still can capture the whole dynamic range of the scene, which our method successfully recovers (c), allowing todiscover details that were not visible in the standard frames.

(a) Events(b) Our Reconstruction(c) Original Image

Figure 19. (a) Previews of some event sequences from the N-Caltech101 dataset [33] which features event sequences converted from theCaltech101 dataset. (b) our reconstructions (from events only) preserve many of the details and statistics of the original images (c). Notethat these datasets feature planar motion (since Caltech101 images were projected on white wall to record the events), which coincides
with the type of motions present in the simulated data, which explains in part the outstanding visual quality of the reconstructions.

0.8

Yaw error [deg]Translation error [m]

1.0

0.6
0.4
0.2
0.0

3.0

7.0

11.0
15.0
Distance traveled [m]

19.0

7
6
5
4
3
2
1
0

Ours (E+I)
UltimateSLAM (E+I)
UltimateSLAM (E+F+I)

3.0

7.0

11.0
Distance traveled [m]

15.0

19.0

1.0
Yaw error [deg]

0.8
0.6
0.4
0.2
0.0

5.0

11.0

16.0
22.0
Distance traveled [m]

27.0

1.0
Yaw error [deg]

0.8
0.6
0.4
0.2
0.0

4.0

9.0

13.0
18.0
Distance traveled [m]

23.0

1.0
Yaw error [deg]

0.8
0.6
0.4
0.2
0.0

6.0

12.0

19.0
25.0
Distance traveled [m]

32.0

1.0
0.8

Yaw error [deg]Translation error [m]Translation error [m]Translation error [m]Translation error [m]Figure 20. Evolution of the overall mean translation error (in meters) and mean rotation error (in degrees), averaged across all the datasets
used in our evaluation.

0.6
0.4
0.2
0.0

2.0

5.0

8.0
11.0
Distance traveled [m]

14.0

8
7
6
5
4
3
2
1
0

3.5
3.0
2.5
2.0
1.5
1.0
0.5
0.0

3.5
3.0
2.5
2.0
1.5
1.0
0.5
0.0

4.5
4.0
3.5
3.0
2.5
2.0
1.5
1.0
0.5
0.0

Ours (E+I)
UltimateSLAM (E+I)
UltimateSLAM (E+F+I)

5.0

11.0

16.0
Distance traveled [m]

22.0

27.0

Ours (E+I)
UltimateSLAM (E+I)
UltimateSLAM (E+F+I)

4.0

9.0

13.0
18.0
Distance traveled [m]23.0

Ours (E+I)
UltimateSLAM (E+I)
UltimateSLAM (E+F+I)

6.0

12.0

19.0
25.0
Distance traveled [m]32.0

Ours (E+I)
UltimateSLAM (E+I)
UltimateSLAM (E+F+I)

2.0

5.0

8.0
11.0
Distance traveled [m]14.0

Figure 21. Evolution of the mean translation error (in meters) and mean rotation error (in degrees), as a function of the travelled distance.
Sequences from top to bottom: ’shapes translation’, ’poster translation’, ’boxes translation’, ’dynamic translation’.

Yaw error [deg]

30

1.5
1.0
0.5
0.0

4.0

9.0

14.0
18.0
Distance traveled [m]25Ours (E+I)

20

UltimateSLAM (E+I)

15

UltimateSLAM (E+F+I)

10
5
0

23.0

4.0

9.0

14.0
18.0
Distance traveled [m]

23.0

12

0.8

10

Ours (E+I)

8

UltimateSLAM (E+I)

6

UltimateSLAM (E+F+I)

Yaw error [deg]

1.0

0.6
0.4
0.2

1.4
1.2
1.0
0.8
0.6
0.4
0.2
0.0

5.0

11.0

17.0
23.0
Distance traveled [m]

6.0

13.0

20.0
27.0
Distance traveled [m]

4
2
0

29.0

Yaw error [deg]

0.0

34.0

7
6
5
4
3
2
1
0

5.0

11.0

17.0
23.0
Distance traveled [m]29.0

Ours (E+I)
UltimateSLAM (E+I)
UltimateSLAM (E+F+I)

6.0

13.0

20.0
Distance traveled [m]

27.0

34.0

6

0.8

5

Ours (E+I)

4

UltimateSLAM (E+I)

3

UltimateSLAM (E+F+I)

Yaw error [deg]

1.0

0.6
0.4
0.2
0.0

3.0

7.0

11.0
15.0
Distance traveled [m]

1.0
0.8
0.6
0.4
0.2
0.0

5.0

10.0

16.0
21.0
Distance traveled [m]

27.0

2
1
0

19.0

Yaw error [deg]Translation error [m]Translation error [m]Translation error [m]Translation error [m]Translation error [m]

2.0

8
7
6
5
4
3
2
1
0

3.0

7.0

11.0
Distance traveled [m]

15.0

19.0

Ours (E+I)
UltimateSLAM (E+I)
UltimateSLAM (E+F+I)

5.0

10.0

16.0
Distance traveled [m]

21.0

27.0

Figure 22. Evolution of the mean translation error (in meters) and mean rotation error (in degrees), as a function of the travelled distance.
Sequences from top to bottom: ’shapes 6dof’, ’poster 6dof’, ’boxes 6dof’, ’dynamic 6dof’, ’hdr boxes’.

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