It’s All Around You: Range-Guided Cylindrical Networkfor 3D Object Detection
Meytal Rapoport-Lavie
Tel-Aviv University

Dan Raviv
Tel-Aviv University

laviemeytal@mail.tau.ac.il

darav@tauex.tau.ac.il

Abstract
Modern perception systems in the field of autonomous
driving rely on 3D data analysis. LiDAR sensors are frequently used to acquire such data due to their increased
resilience to different lighting conditions. Although rotating LiDAR scanners produce ring-shaped patterns in space,most networks analyze their data using an orthogonal voxel
sampling strategy. This work presents a novel approach
for analyzing 3D data produced by 360-degree depth scanners, utilizing a more suitable coordinate system, which isaligned with the scanning pattern. Furthermore, we introduce a novel notion of range-guided convolutions, adapting
the receptive field by distance from the ego vehicle and theobject’s scale. Our network demonstrates powerful resultson the competitive nuScenes 3D object detection challenge,
comparable to current state-of-the-art architectures.

1. Introduction
Robustness is a crucial requirement for visual perception solutions in autonomous driving systems. Such systems
must be resilient to various lighting scenarios and withstandharsh weather conditions without compromising their performance. Therefore, extending the traditional RGB cameras with additional sensors, such as LiDAR, is a vital step
towards achieving this goal.
The inclusion of depth information, which allows capturing the three-dimensional structure of the vehicle’s environment, is a key feature for ensuring robustness while maintaining high accuracy. Modern LiDAR acquisition sensors
provide meaningful information not only for avoiding imminent collisions, but also to perceive the environment as
good as image-based data and even surpass it under poor
lighting conditions.
In the field of autonomous driving, there is a variety ofmethods to handle point clouds. The most common andefficient approach is to transform the point cloud into a regular representation, such as 2D bird-eye-view (BEV) vox-

Figure 1. Top-View of a nuScenes dataset sample with Ground
Truth detections. First, it can be easily observed that the LiDAR
sensor has a ring-shaped pattern output. Second, the Cylindrical
Coordinate system maintains 9 neighbors and 27 neighbors per
voxel (including itself), in 2D and 3D respectively, the same as theclassical Cartesian system. This enables us to employ the classicalconvolution layers without almost any alteration.

els [13, 7, 27, 14] or 3D voxels [24, 31, 8, 26]. Following
the architecture of PointNet and PointNet++ [4, 21], someworks have proposed replacing the voxel-based methodwith a point-based one. This approach, however, was foundto be computationally intensive. Recent papers have suggested combining both point-based and voxel-based methods into a single network [23, 16]. However, whether combining point-based methods or not, in 2D or 3D, these methods use the Cartesian coordinate system as their grid.
In this paper, we argue that projecting the points ontoa Cartesian space disregards the circular nature of the rawOur main contributions are as follows:• We present the first end-to-end cylindrical coordinates
perception architecture for autonomous systems that
includes orientation and velocity estimation.
• We present a novel range-guided convolution block toadjust the network’s receptive field according to an object’s range and scale, and adapting its learned featuresby its distance from the ego-vehicle.
• We provide new insights, challenges and solutions fortraining deep networks in cylindrical coordinates.
• Our network achieves comparable results to current
state-of-the-art networks on the competitive nuScenes
3D object detection benchmark.

2. Related works
Cartesian Based 3D Object Detection Methods: Most
state-of-the-art methods for 3D object detection project
the point clouds onto a Cartesian coordinate grid. Earlier approaches employed the BEV projection to utilize efficient 2D convolution layers to process the point clouddata [13, 7]. In a later work, VoxelNet [31] was first to introduce 3D voxels in the field of object detection, whereas
SECOND [26] improved this method by applying sparse
3D convolutions to accelerate running time. The majorityof cutting edge methods [24, 23], including nuScenes’ former state-of-the-art, CBGS [32], have adopted the practiceof processing point cloud using sparse three-dimensional
convolutions after projecting it into the traditional Cartesianvoxel space.

Mean Points per Voxel
Cartesian
Cylindrical101

Points per voxel (log)data provided by a rotating emitter as can be easily seen in
Figure 1. We show that in order to perceive and preserve
the additional information gained from a LiDAR sensor,
one may leverage a more appropriate coordinate system,
namely the Cylindrical coordinates system. Furthermore,
the Cylindrical coordinate system allows us to align the entire network towards the sensor’s point of view. Moreover,the closer an object is to the ego vehicle, the more points
correspond to it in the point cloud. Therefore, we suggest
adjusting the size of the voxels to the distance from the sensor, as is done naturally by the Cylindrical space. As shownin Figure 2, using the cylindrical system, we get a better distribution of average points per voxel over distance, leading
to a better resolution after the voxelization step. In addition, we propose a novel guiding unit, which orchestrates
convolutions by their range from the ego vehicle. We further explore the challenges for a ”self-oriented” network tolearn angle and velocity information and present the necessary solutions to overcome them successfully.

100

10 1

0

10

20

30

40

50

Range (m)Figure 2. Mean number of points per voxel (logarithmic) as afunction of range. There is a high inverse correlation between thenumber of points and their range from the sensor. Since the Cartesian voxels are of the same size in all ranges, we lose precision in
the nearby distance. On the other hand, with Cylindrical coordinate we receive a better spatial distribution which leads to a better
resolution after the voxelization step.

Non-Cartesian Based Methods: PolarNet [30] was thefirst paper to suggest quantizing LiDAR data in a nonCartesian coordinate system. Their Polar-BEV network, designed for semantic segmentation on the SemanticKITTI [1]challenge, have shown improvement with respect to traditional Cartesian methods. Unlike semantic segmentation
tasks, 3D detection challenges, such as nuScenes, require
the processing of additional object orientation and velocity
features. As direction information plays a significant rolein such tasks, they are directly affected by the change in thecoordinate system and therefore need to be treated with special care. A recently published paper, CVCNet [22], also
quantizes the input LiDAR point cloud utilizing the polar
grid, and then divide it into two separate views - BEV voxels (r, θ) and range-view voxels (θ, ϕ). Although the Polar
coordinate system pr, θ, ϕq, is different than the cylindricalone pρ, θ, zq, it shares some of the challenges, when comparing both to the Cartesian system. Nevertheless, unlike
our paper, the authors overlooked issues regarding the orientation and velocity predictions, and the change in the receptive field caused by the change in voxel size.
3D Object Detection Methods with Focus on Range:
LaserNet [19] has introduced the concept of range-image
input to the field of 3D detection, naturally emphasizing an
object’s distance from the ego vehicle as one of its key features. Despite its computational efficiency, it was generally outperformed by voxel-based methods. A later publication [2] suggested a method to address range-image’s
problem of scale variance, the fact that near distance objects
appear larger in the image view. They applied dilated convolution [5, 25] directly to the range-image input, adjustingeach pixel’s dilation rate as a function of its range.

Cylinder Map to Input Array for Objects with same θdir

Figure 3. Illustration of the Cylindrical map translated into the network’s 2D array. Shown here are two identical cars placed at
the same distance from the ego vehicle but with different θcenter . Since we are working from the sensor’s point of view, we can seethat it translates differently to the 2D array. The network observes the yellow car from the side as the purple car from behind, just as thesensor/driver does. Thus, in order for the network to learn the object’s orientation appropriately, a modification is required.

In recent paper RangeRCNN [16] tried to benefit fromboth range view and BEV. RangeRCNN [16] argued that
although range-image is insufficient by itself, incorporating it as a source of initial feature extraction, before transforming the data into BEV architectures can be effective.
This method of enhancing BEV with range-image features
achieves state-of-the-art results on the KITTI dataset [10].
In our work, we use range information (i.e ρ, the 2D distance in the XY-plane) to guide the 3D convolution layers.
In this manner, we gain additional benefit from its data as
an initial feature, allowing the network to comprehend objects differently depending on their distance from the ego
vehicle. Furthermore, we use range information to manage
the receptive field of the voxels and consequently overcome
their scale variance in the Cylindrical-coordinate voxels.

3. Challenges in Cylindrical Coordinates
The Cylindrical-coordinate grid differs from the Cartesian in many aspects. First of all, it is a self-oriented system.
Consequently, identical objects placed at different positions
relative to the sensor location will be presented differently
along the network’s channels. For this reason, in order toadapt the network to the sensor’s point of view, certain challenges have to be addressed.
In contrast to Cartesian coordinates BEV, the size of a
Cylindrical voxel varies depending on its range. Thus, two
identical objects can spread over a different number of voxels, depending on their center location relative to the ego
vehicle. Likewise, the orientation of an object toward theego vehicle might also affect the number of voxels it occupies in the Cylindrical grid. In general, objects spread on alarger amount of voxels, require a wider receptive field inorder to detect them.
The Cartesian and Cylindrical-coordinate systems differ
in the way they perceive the orientation of objects. The net-

work’s point of view can be equated with the perspective ofa passenger in the ego-vehicle, observing two identical cars,
one of which is in front of the vehicle while the other is on
its side. As illustrated in Figure 3, both yellow and purple cars, are of the same size, facing the same way, and at
the same range. However, the difference in their θ-centerscauses them to be mapped differently to the network’s input arrays with an emphasis on the direction. As shown,the purple and yellow cars are no longer facing the same
direction in the 2D input array of our network. In order forthe network to learn its orientation accordingly, it should be
modified in a proper manner.
The same principle applies to the velocity’s direction.
For example, an object moving along a certain axis willseem to the Cartesian-based network to be moving alongthat axis regardless of its location on the map. This behavior manifests because the axes of Cartesian coordinates arealigned to those of every other object on the map. However, for the Cylindrical coordinates, this is not the case,and therefore additional adjustments are necessary.
Additionally, since the θ axis is inherently cyclical (´πis also π), objects may spread simultaneously over voxels
at both the beginning and the end of the network’s input
arrays. This is a further deviation from the Cartesian systemthat needs to be taken into account in order to accomplish
the transition between the two systems.
In conclusion, transferring from a Cartesian coordinate
system to a cylindrical one reveals our networks’ uniquechallenges. To take full advantage of cylindrical coordinates, we must modify the architecture and its learned outputs in order to overcome the aforementioned challenges.

4. Method
After our preprocess of the data our network consists ofthree learning blocks: (1) Range-Guided Backbone, which

Conv3d
Block

Conv3dStrideBlock

4 Conv3d
Block

Guiding
Unit

Conv3dStrideBlock

Guiding
Unit

Conv3dStrideBlock

Guiding
Unit

Conv3dStrideBlockFull Input

Conv3dStrideBlock

Conv3dBlock

Conv3dStrideBlock

Conv3dStrideBlockOnly Range
FeatureFigure 4. Illustration of the Range-Guided Backbone: The guided backbone is shown in black, the range guiding backbone is shown ina green dashed line and the guiding units are shown in blue.
Resnet connection(Full)

Conv3dBlock

Conv3dBlock

Conv3dBlock

Conv3dBlockConcatConv1dBlock

Conv1dBlock

Conv1dBlock

Conv1dConv1dBlock

Guiding
Unit(Range)Figure 5. Illustration of the Guiding Unit: On the range input we use four separate 1D convolutions. The output of each one is multipliedby the output of its parallel convolution layer on the full input and then concatenated together. In order to achieve the original number offeatures, we apply 1D convolution with a residual [12] connection.

receives the 3D voxelized input and constructs an output
of a flattened 2D map by utilizing our novel guiding units,(2) off-the-shelf Region Proposal Network and (3) a MultiGroup Center Head that outputs a heat-map per class anda variety of predicted parameters for each detected object.
Our code is based on the old open source version of CenterPoint [28].
Input: Although the raw output of a LiDAR sensor isgiven in range and two angles, the nuScenes dataset provides the point cloud sweeps already transformed to Cartesian coordinates. Each point is assigned with two additionalfeatures: intensity, which is taken directly from the sensor,
and ∆t, which relates to the lag-time between each sweep toits key-frame sweep. We followed the rules of the nuScenes
detection benchmark by taking ten sequential sweeps (totaling 0.5 seconds). We then quantize it according to the cylindrical voxel resolution and average on each voxel across allfeatures.
Range-Guided Backbone: The range-guided backbone
is the first deep network block of our proposed solution. As
similar objects appear across a different number of voxels
dependent on the range, we propose to control the convolu-

tions in the main pipeline using range-guided mechanism.
The motivation for range guidance is two-fold; First, the
LiDAR transmitter releases rapid pulses of laser light acrossa pre-defined spatial pattern. Using a synced sensor, it measures the time it takes for each pulse to reflect back fromthe object it hits. Then, we receive multiple hits per object,which depends on its distance. Meaning, nearby objects
will be over-represented in contrast to far away objects, as
can be seen in Figure 2 leading to significant deviation inclose and distant objects statistics. The second reason relates to the voxel size. As noted earlier (see Section 3), in
Cylindrical coordinates the voxel size increases with range.
Which means nearby objects need a wider receptive field
than far away objects.
Our main guided backbone is built from four 3D convolution with stride 2 and three guiding units in between
as can be seen in Figure 4. The range guiding backbone ismade of a convolution pipeline fed by only range information (meaning only the range feature of each voxel), controlling the weights multiplying each of the consecutive convolutions output running on the main pipeline.
Specifically, the range backbone is parallel to the main
pipeline. In each guiding unit, the output of each 3D convolution running on the main pipeline is multiplied by theCylinder Map to Input Array for Objects with Same Modified θ̄dir

Figure 6. Illustration of the Cylindrical map translated into the network’s 2D array. Here shown three identical cars of the same size,
but with different centers. They have different θdir as well, but share the same θ̄dir (defined in equation (3)), the orientation as viewed
from the sensor’s perspective. In addition, we see that the closer the vehicle is to the sensor, the more voxels it occupies in the θ axis. The
circular nature of the θ axis can also be observed here, where the magenta car is spread over both sides of the 2D input array.

output of a 1 x 1 convolution which directly operates on
the range feature. The product of four such multiplications
are concatenated and then reduced in dimensions by a 1 x 1
convolution operator. See Figure 5 for visual aid.
The design of the guiding unit allows the neural network
to learn different receptive fields, while the 3D convolution layers in between contribute to its learning capabilities of the spatial information of neighboring voxels. In our
Cylindrical network, this backbone will serve mainly the far
range where the receptive field needs to be smaller.
Multi Group Center Head: For this block, we follow thehead architecture of CenterPoint [28]. However, in order toovercome the rest of the aforementioned challenges whenusing cylindrical coordinates, it is necessary to make further
modifications for this block.
The original head outputs a K-channel 2D heatmap to
indicate the centers of the K-class objects in the x-y plane.
In addition to the heatmap, the network outputs ten different scalars for each detection: pdx, dyq which is thedelta to the object’s center from the heatmap coordinates,
pw, l, hq which indicate the object width, length and height,
pzcenter q for the object’s center in the Z-axis, pvx , vy q for
the object’s velocity in X and Y axes respectively andpcos θdir , sin θdir q where θdir is the heading angle of theobject.
The ground truth heatmaps for training are built by rendering a Gaussian around each object’s center. CenterPoint [28] uses symmetric 2D Gaussian since the X and Y
axes share the same resolution, and calculate the radius using the following equation:
  \label {eq:sigma} \sigma =\max (f(wl,r),\tau ) 

(1)

Where f is the radius function defined in CornerNet [15],τ is the smallest allowable Gaussian radius and w, l are thewidth and length of the detected object in number of voxels,
meaning the result of w and l divided by the voxel’s size.
In our setup, we can not use symmetric Gaussian anymore thus we split it into 2D Gaussian with different radiifor each axis which are calculated separately by eq.(1). We
then calculate the size of a voxel in the θ-axis by the following formula:  V_{\theta size} = 2r_{center}\sin \left (\frac {\theta _{center}}{2}\right ) (2)

Where rcenter , and θcenter are the coordinates of the object’s center in the r and θ dimensions respectively.
While the transformation of the heatmap coordinates andthe deltas from Cartesian to Cylindrical coordinates is quite
simple, the heading angle and velocity outputs are much
more complex and require a deep understanding of the network’s view.
The θdir in which our Cylinder-network observes the object depends not only on the θdir of the object but also on
the θcenter of the object. An object facing forward withθcenter “ π{2 will be interpreted on the 2d map the same
way as an object whose center is right in front of the egovehicle and facing θdir “ ´π{2 will, see Figure 6. Therefore, the θdir we wish to learn is as follows:  \label {equation:dir} \Bar \theta _{dir} = \theta _{dir} - \theta _{center} 

(3)

Where θcenter is the θ coordinate of the object’s center.
Our use of θ̄dir instead of θdir gives us a significant advantage over the Cartesian coordinate system. As demonstrated in Figure 7, points are distributed differently between similar cars with respect to their θcenter . Although
the two cars share identical θdir in the classic system, thepoints are scattered on different parts of each vehicle. As

Then, we divide that into two learnable outputs according to our self-cylindrical coordinates as before:
  \label {equation:velo_learn} \begin {split} V_{\theta } &= V_{abs}\sin (\Bar \theta _{velocity})\\ V_{r} &= V_{abs}\cos (\Bar \theta _{velocity}). \end {split} (7)Losses: We use L1 regression loss for the local offset
pdr, dθ), orientation pθr , θθ q as defined in equation (4) andvelocity pVr , Vθ q as defined in equation (7). As for size
pw, l, hq and zcenter we use log-space L1 regression, andfor the heatmap supervision a focal loss [17] with α “ 2and β “ 4 is used.
The overall loss of our network is as follows:
  L=L_{heatmap}+\lambda _{reg}L_{reg} Figure 7. A single frame of nuScenes scene, demonstrating theCartesian system’s drawback: Although the green and blue cars
share many characteristics, in particular that they both have a similar orientation - the pattern in which they are covered with point
clouds is significantly different. By applying the modifications described in equation 3, this difference no longer poses a challenge
to our Cylindrical network.

the blue bound car is mostly ”hit” on its front, the points”hitting” the green marked car mainly concentrate on its
left side. In our modified system, for instance, when mostof the object’s cloud points cover its rear, the value of its
θ̄dir “ 0, when the majority of the points appear on its rightside, θ̄dir “ π{2, and so forth. We believe it allows our
system a more adequate comprehension of the scene.
Now we can separate the new θ̄dir into two orthogonalcomponents:
  \label {equation:dir_learn} \begin {split} \theta _{\theta } = \sin (\Bar \theta _{dir}) \\ \theta _{r} = \cos (\Bar \theta _{dir}) \end {split} (4)For each detection, one is also required to output the object’s velocity. As opposed to θ, the required velocity forthis benchmark is already divided into the classical coordinates, meaning the metrics of this benchmark evaluates vx
and vy and they are given as ground truth. Thus, an additional step is required:  \begin {split} V_{abs} &= \sqrt {v_{x}^2 + v_{y}^2} \\ V_{dir} &= \arctan \left ( \frac {v_{x}}{v_{y}} \right ) \end {split} (5)

Where we use Vx as the numerator and Vy as the denominator to be aligned with nuScenes coordinate system.
We then apply the same transformation we did in equation (3) for velocity:
  \label {equation:velo} \Bar \theta _{velocity} = V_{dir} - \theta _{center} 

(6)

(8)

Where Lheatmap is the focal loss for the heatmap and Lregand λreg are the regression losses mentioned above andtheir loss weight accordingly.
Circular Padding: We address the circularity of the θaxis of the neural network by substituting zero-padding withcircular padding for the θ-axis in all of the convolution layers throughout the entire network, both 2D and 3D.
By applying all of the above, we have overcome the challenges posed by the cylindrical coordinate network and can
now capitalize on its advantages.

5. Experiments
Dataset: We evaluate our method on the nuScenes dataset
for 3D object detection [3]. The dataset contains 700 training scenes, 150 validation scenes, and 150 test scenes, each
20 seconds long with spinning 32 lanes LiDAR at 20 frames
per second. The latest nuScenes benchmark requires the detection of 10 different classes with a long-tail distribution:car, bus, construction vehicle, trailer, truck, pedestrian, motorcycle, bicycle, traffic cone and barrier.
Evaluation Metrics: The nuScenes Detection Score(NDS) is a weighted sum of mean Average Precision(mAP) [9] and several True Positive (TP) metrics. The
mAP is calculated as an average of four mAP with distancethreshold of: (0.5m, 1m, 2m and 4m), and the TP metrics are calculated between the prediction and its matchedground truth the under the 2m threshold:
1. Average Translation Error (ATE): The Euclidean distance in meters between the two centers.
2. Average Scale Error (ASE): Calculated as (1 - IOU)
after aligning the centers and orientation of both.
3. Average Orientation Error (AOE): Smallest yaw angle
difference (θdir ).

Method

mAPÒ

mATEÓ

mASEÓ

mAOEÓ

mAVEÓ

mAAEÓ

NDSÒ

CBGS [32]CenterPoint [28]CyliNet RG (ours)

0.499
0.591
0.576

0.335
0.277
0.283

0.256
0.251
0.253

0.323
0.269
0.291

0.251
0.258
0.268

0.197
0.189
0.180

0.613
0.671
0.661

Table 1. nuScenes Experiment Results on Validation set: The results of CBGS and CenterPoint as shown on their github. Top results
are in bold, second place is underlined.

Method

Car

Truck

Bus

Trailer

CV

Ped

Motor

BicycleAll

CenterPoint [28]CyliNet RG (ours)

3.676
2.610

2.637
1.485

1.468
1.273

14.36412.964

9.197
7.267

0.642
0.561

2.641
3.333

3.155
3.974

3.051
2.160

Improvement by %:28.98243.69613.2919.743

20.98512.535-26.207-13.06729.223

Table 2. Flip Orientation Error on Validation set: Percentage per class for ”flip orientation” error (i.e predicted orientation for an object
is nearly 180° from the GT orientation). CV stands for Construction Vehicle, Ped for pedestrian and Motor for Motorcycle.

4. Average Velocity Error (AVE): Absolute velocity error for vx and vy separately, measured in meters per
second.
5. Average Attribute Error (AAE): Calculated as 1 - acc,
where acc is the attribute classification accuracy.
The final NDS is calculated as follows:λreg to be 0.25. During inference, we keep the top 500 detection proposals per sample. Afterward, we filter out all
proposals with a detection score lower than 0.1 and apply
maximum suppression (NMS) with an IOU threshold of 0.1
for all classes except for the car class where we use 0.2. Ultimately, we limit the maximum output to 83 proposals pergroup.

5.1. Results
 NDS = 5mAP+ \sum _{i=1}^{5} \max (1-TP_i, 0.0)
NuScenes has recently added a new hidden evaluation
metric to their evaluation server, called Planning KLDivergence [20] (PKL). The PKL metric measures perception performance by measuring differences between how aplanner would plan when given detections from a detector
instead of human-labeled detections. A larger PKL score
corresponds to poorer performance. On the test set, thismetric will be displayed.
Implementations Details: Following the nuScenes
benchmark, we set the detection range to r1m, 53.8ms
for the range axis, r´π, πs for the θ axis and r´5m, 3ms
for the Z-axis. Utilizing the structure of the cylindricalcoordinates, we do not need extra spaces in our input map,in order to detect an object within the required 50m range.
With the choice of r0.075m, π{600, 0.2ms for the voxels size in each axis respectively, our network input size is
704 x 1200 x 40 voxels. In our range-guided backbone weuse four times stride 2 for the range axis, and three times
for the θ axis, resulting in 88 x 300 output size. Due tothe cylindrical shape of our output map, it is almost 20%
smaller than CenterPoint’s [28] output map. We train themodel with a batch size of 56 for 20 epochs on 8 RTX8000
GPUs. We have chosen adamW [18] optimizer with onecycle policy [11], LR max 0.0035 with division factor 10,
momentum from 0.85 to 0.95, weight decay 0.01 and chose

We present our results on both nuScenes validation set
shown in Table 1 and nuScenes test set shown in Table 3.
The test results were obtained from nuScenes official evaluation server, where test sample annotations remain hidden.
Our single model on the LiDAR track outperforms all methods but a single one, both for NDS and PKL scores. Our network also outperforms both of CVCNet’s [22] submissions
by a very large margin. Although they used the polar coordinate system to quantize the input points cloud, and not
the cylindrical one as we did, they are still the only othermethod that uses another coordinate system than the Cartesian, and therefore we believe a comparison is worthwhile.
In NDS we achieved a score of 0.661 versus 0.642 and
0.644 while in the PKL metric (lower is better) we achieved
a score of 0.708 as opposed to 0.935 and 0.919 (around 30%
improvement).
Moreover, we examined the mAOE and we noticed afundamental improvement in flip orientation error reduction. Due to the structure of the roads, there are high picks
in the orientation distribution for p0, ˘π{2, ˘πq, especiallyfor vehicles. This gives the Cartesian coordinate system anadvantage, especially for avoiding small errors in orientation detection, but with the price of increase flip error. Our
network on the other hand is comparable on average measuring rotation error, but due to the nature of Cylindricalcoordinates point of view we better identify the directionof each element, which is a critical unit for the planners.
With the modification we conduct for orientation (eq.3) our

Method

mAPÒ

mATEÓ

mASEÓ

mAOEÓ

mAVEÓ

mAAEÓ

NDSÒ

PKLÓ

CBGS [32]CVCNet single [22]CVCNet single V2 [22]MMDetection3D [29]HotSpotNet-0.1m [6]CenterPoint single[28]

0.528
0.558
0.553
0.575
0.593
0.603

0.300
0.300
0.300
0.316
0.274
0.262

0.247
0.248
0.244
0.256
0.239
0.239

0.379
0.431
0.389
0.409
0.384
0.361

0.245
0.269
0.268
0.236
0.333
0.288

0.140
0.119
0.122
0.124
0.133
0.136

0.633
0.642
0.644
0.653
0.660
0.673

0.766
0.935
0.919
0.710
0.851
0.690

CyliNet RG single (ours)

0.585

0.272

0.243

0.383

0.293

0.126

0.661

0.708

Table 3. nuScenes Experiment Results on Test set: Top results for single model on the LiDAR track, taken from the nuScenes website.
Top results are in bold, second place is underlined.

CC.mod.

0.5mÒ

1mÒ

2mÒ

4mÒ

mAPÒ

mATEÓ

mASEÓ

mAOEÓ

mAVEÓ

mAAEÓ

NDSÒ

ˆ
‘

0.468
0.469

0.561
0.562

0.618
0.623

0.648
0.651

0.574
0.576

0.282
0.283

0.250
0.253

0.314
0.291

0.312
0.268

0.185
0.180

0.652
0.661

Table 4. Ablation Experiment on nuScenes Validation set: Cylindrical-coordinate modifications (CC.mod.) refers to modification
explained on subsection Multi Group Center Head under section Method 4 for both orientation (θ̄dir ) and velocity (θ̄velocity ). Notice, how
our CC.mod. significantly improves performance for both, orientation (mAOE) and velocity (mAVE).

Cylindrical network is learning the relative orientation asshown and explained in Figures 6 and 7, therefore whenlearning for example θ̄dir “ 0 it is always related the sameorientation in space, in this case a rear view of the object.
As Table 2 indicates, this modification led to a decrease in”flip orientation” error. In this table we show the percentage
per class for such error, averaging on all four of nuScenes
mAP metrics. As can be seen, our network is decreasing
the error rate in almost all classes by a large margin, andimproves results by 29.2% when combining all classes inall mAP together.

5.2. Ablation studies
We further conduct our ablation studies on the nuScenes
validation set.
First, we demonstrate the significance of our Cylindricalcoordinate modifications detailed in Multi-Group Center
Head on section 4. Table 4 shows the result for two identical networks, trained in the same way, both utilizing circular padding. In the first network, we did not use our velocity
and orientation modifications, while in the second, we did.
As expected, the most significant improvement in the second network’s performance can be observed in the velocity
and orientation metrics. The mean average orientation error decreases from 0.314 to 0.291 and the mean averagevelocity error decreases from 0.312 to 0.268 (a 15% improvement). Additionally, all the mAP metrics indicate anincrease as well, and we see a significant increase in the
NDS from 0.652 to 0.661.
Moreover, we show the effectiveness of our rangeguided backbone to reduce the receptive field for larger
distances and for smaller objects. For range r30m, 50ms

over all classes we achieve NDS of 0.485 when the guidance mechanism is applied, and 0.478 when omitted while
using the heavily optimized backbone of CBGS [32]. For
the entire range we also witness an improvement in NDS
from 0.658 when omitted versus 0.661 when applied. Aninteresting point to note is that the range information is included in our Cylindrical network even without the guiding
units. Nevertheless, we see here that an additional use of it
as convolution weight guidance in the first block, is proving
beneficial for the network.

6. Conclusions
In this paper, we explore the vast potential of a
Cylindrical-coordinate based network for representing LiDAR point clouds in an outdoor scene. We present the firstend-to-end framework for 3D object detection in those coordinates along with a novel range-guided backbone that controls the receptive field based on the range information. We
further report a fundamental improvement in flip orientation
error which is critical to the planner in identifying potential risks. Our method achieves strong results on the highly
challenging nuScenes dataset and lays the foundation forfurther Cylindrical methods in this domain.

7. Acknowledgment
This work is partially funded by the Zimin Institute forEngineering Solutions Advancing Better Lives, the Israeli
consortiums for soft robotics and autonomous driving, theNicholas and Elizabeth Slezak Super Center for Cardiac Research and Biomedical Engineering at Tel Aviv University
and TAU Science Data and AI Center.

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