The ApolloScape Dataset for Autonomous Driving

# The ApolloScape Dataset for Autonomous Driving

## Abstract

Scene parsing aims to assign a class (semantic) label for each pixel in an image. It is a comprehensive analysis of an image. Given the rise of autonomous driving, pixel-accurate environmental perception is expected to be a key enabling technical piece. However, providing a large scale dataset for the design and evaluation of scene parsing algorithms, in particular for outdoor scenes, has been difficult. The per-pixel labelling process is prohibitively expensive, limiting the scale of existing ones. In this paper, we present a large-scale open dataset, ApolloScape, that consists of RGB videos and corresponding dense 3D point clouds. Comparing with existing datasets, our dataset has the following unique properties. The first is its scale, our initial release contains over 140K images -- each with its per-pixel semantic mask, up to 1M is scheduled. The second is its complexity. Captured in various traffic conditions, the number of moving objects averages from tens to over one hundred (Figure 1). And the third is the 3D attribute, each image is tagged with high-accuracy pose information at cm accuracy and the static background point cloud has mm relative accuracy. We are able to label these many images by an interactive and efficient labelling pipeline that utilizes the high-quality 3D point cloud. Moreover, our dataset also contains different lane markings based on the lane colors and styles. We expect our new dataset can deeply benefit various autonomous driving related applications that include but not limited to 2D/3D scene understanding, localization, transfer learning, and driving simulation.

\cvprfinalcopy

## 1 Introduction

Semantic segmentation, or scene parsing, of urban street views is one of major research topics in the area of autonomous driving. A number of datasets have been collected in various cities in recent years, aiming to increase variability and complexity of urban street views. The Cambridge-driving Labeled Video database (CamVid) [1] could be the first dataset with semantic annotated videos. The size of the dataset is relatively small, which contains 701 manually annotated images with 32 semantic classes captured from a driving vehicle. The KITTI Vision Benchmark Suite [4] collected and labeled a dataset for different computer vision tasks such as stereo, optical flow, 2D/3D object detection and tracking. For instance, 7,481 training and 7,518 test images are annotated by 2D and 3D bounding boxes for the tasks of object detection and object orientation estimation. This dataset contains up to 15 cars and 30 pedestrians in each image. However, pixel-level annotations are only made partially by third parties without quality controls. As a result, semantic segmentation benchmark is not provided directly. The Cityscapes Dataset [2] focuses on 2D semantic segmentation of street views that contains 30 classes, 5,000 images with fine annotations, and 20,000 images with coarse annotations. Although video frames are available, only one image (20th image in each video snippet) is annotated. The TorontoCity benchmark [12] collects LIDAR data and images including stereo and panoramas from both drones and moving vehicles. Currently, this could be the largest dataset, which covers the greater Toronto area. However, as mentioned by authors, it is not possible to manually label this scale of dataset. Therefore, only two semantic classes, i.e., building footprints and roads, are provided as the benchmark task of the segmentation.

In this paper, we present an on-going project aimed to provide an open large-scale comprehensive dataset for urban street views. The eventual dataset will include RGB videos with millions high resolution image and per pixel annotation, survey-grade dense 3D points with semantic segmentation, stereoscopic video with rare events, night-vision sensors. Our on-going collection will further cover a wide range of environment, weather, and traffic conditions. Comparing with existing datasets, our dataset has the following characteristics:

1. The first subset, 143,906 image frames with pixel annotations, has been released. We divide our dataset into easy, moderate, and hard subsets. The difficulty levels are measured based on number of vehicles and pedestrians per image that often indicates the scene complexity. Our goal is to capture and annotate around one million video frames and corresponding 3D point clouds.

2. Our dataset has survey-grade dense 3D point cloud for static objects. A rendered depth map is associated with each image, creating the first pixel-annotated RGB-D video for outdoor scenes.

3. In addition to typical object annotations, our dataset also contains fine grain labelling of lane markings (with 28 classes).

4. An interactive and efficient 2D/3D joint-labelling pipeline is designed for this dataset. On average it saves 70% labeling time. Based on our labelling pipeline, all the 3D point clouds will be assigned with above annotations. Therefore, our dataset is the first open dataset of street views containing 3D annotations.

5. The instance-level annotations are available for video frames, which are especially useful to design spatial-temporal models for predication, tracking, and behavior analysis of movable objects.

We have already release the first batch of our data set at http://apolloscape.auto. More data will be added periodically.

## 2 Acquisition

Riegl VMX-1HA [9] is used as our acquisition system that mainly consists of two VUX-1HA laser scanners ( FOV, range from 1.2m up to 420m with target reflectivity larger than 80%), VMX-CS6 camera system (two front cameras are used with resolution ), and the measuring head with IMU/GNSS (position accuracy mm, roll & pitch accuracy , and heading accuracy ).

The laser scanners utilizes two laser beams to scan its surroundings vertically that are similar to the push-broom cameras. Comparing with common-used Velodyne HDL-64E [11], the scanners are able to acquire higher density of point clouds and obtain higher measuring accuracy / precision (5mm / 3mm). The whole system has been internally calibrated and synchronized. It is mounted on the top of a mid-size SUV (Figure 2) that drives at the speed of 30km per hour and the cameras are triggered every one meter. However, the acquired point clouds of moving objects could be highly distorted or even completely missing.

## 3 Dataset

Currently, we have released the first part of the dataset that contains 143,906 video frames and corresponding pixel-level annotations for semantic segmentation task. In the released dataset, 89,430 instance-level annotations for movable objects are further provided, which could be particularly useful for instance-level video object segmentation and predication. Table 2 shows a comparison of several key properties between our dataset and other street-view datasets.

The dataset is collected from different traces that present easy, moderate, and heavy scene complexities. Similar to the Cityscapes, we measure the scene complexity based on the amount of movable objects, such as person and vehicles. Table 1 compares the scene complexities between our dataset and other open datasets [2, 4]. In the table, we also show the statistics for the individual classes of movable objects. We find that both total number and average number of object instances are much higher than those of other datasets. More importantly, our dataset contains more challenging environments are shown in Figure 3. For instance, two extreme lighting conditions (e.g., dark and bright) appear in the same image that could be caused by the shadow of an overpass. Reflections of multiple nearby vehicles on a bus surface may fail many instance-level segmentation algorithms. We will continue release more data in near future with large diversities of location, traffic conditions, and weathers.

### 3.1 Specifications

We annotate 25 different labels covered by five groups. Table 3 gives the details of these labels. The IDs shown in the table are the IDs used for training. The value 255 indicates the ignoring labels that currently are not evaluated during the testing phase. The specifications of the classes are similar to the cityscape dataset with several differences. For instance, we add one new tricycle" class that is a quite popular transport in the east Asia countries. This class covers all kinds of three-wheeled vehicles that could be both motorized and human-powered. While the rider class in the cityscape is defined as the person on means of transport, we consider the person and the transport as a single moving object and merge them together as a single class.

We also annotate 28 different lane markings that currently are not available in existing open datasets. The annotations are defined based on lane boundary attributes including color (e.g., white and yellow) and type (e.g., solid and broken). Table 4 gives detailed information of these lane markings. We separate visible old marking" from other classes, which represents the ghost marking" that is visible remnants of old lane marking. This kind of marking is a persistent problem in many countries that could cause confusion even for human drivers.

## 4 Labeling Process

In order to make our labeling of video frames accurate and efficient, we develop a labeling pipeline as shown in Figure 4. The pipeline mainly consists of two stages, 3D labeling and 2D labeling, to handle static background/objects and moving objects respectively. The basic idea of our pipeline is similar to the one described in [14], while some key techniques used in our pipeline are different. For instance, the algorithms to handle moving objects are different.

The point clouds of moving objects could be highly distorted as mentioned in the Section 2. Therefore, we take three steps to eliminate this part of point clouds: 1) scan the same road segment multiple rounds; 2) align these point clouds; 3) remove the points based on the temporal consistency. Note that additional control points could be added to further improve alignment performance in the step 2).

In order to speed up the 3D labeling process, we first over-segment point clouds into point clusters based on spatial distances and normal directions. Then, we label these point clusters manually. Based on part of labeled data, we also re-train the PointNet++ model [7] to pre-segment the point clouds that could achieve better segmentation performance. As these preliminary results still cannot be used directly as the ground truth, we refine the results by fixing wrong annotations manually. The wrong annotations often occur around the object boundaries. The 3D labeling tool is developed to integrate the above modules together. The user interface design of the tool as shown in Figure 5 further speed up the labeling process, which includes 3D rotation, (inverse-)selection by polygons, matching between point clouds and camera views, and so on.

Once the 3D annotations are generated, the annotations of static background/objects for all the 2D image frames are generated automatically by 3D-2D projections. The splatting techniques in computer graphics are further applied to handle unlabeled pixels that are often caused by missing points or strong reflections.

To speed up the 2D labeling process, we first train a CNN network for movable objects [13] and pre-segment the 2D images. Another labeling tool for 2D images is developed to fix or refine the segmentation results. Again, the wrong annotations often occur around the object boundaries that could be caused by merge/split of multiple objects and harsh lighting conditions. Our 2D labeling tool is designed so that the control points of the boundaries could be easily selected and adjusted.

Figure 6 presents an example of 2D annotated image. Notice that some background classes such as fence, traffic light, and vegetation are able to be annotated in details. In other datasets, these classes could be ambiguous caused by occlusions or labeled as a whole region in order to save labeling efforts.

## 5 Benchmark Suite

Given 3D annotations, 2D pixel and instance-level annotations, background depth maps, camera pose information, a number of tasks could be defined. In current release, we mainly focus on the 2D image parsing task. We would like to add more tasks in near future.

### 5.1 Image Parsing Metric

Given set of ground truth labels and set of predicted labels , the intersect over union (IoU) metric [3] for a class is computed as,

 IoU(S,S∗,c) =∑Ni=1tp(i,c)∑Ni=1(tp(i,c)+fp(i,c)+tn(i,c)) (1) tp(i,c) =∑p1(Li(p)=c⋅^Li(p)=c) fp(i,c) =∑p1(Li(k)≠c⋅^Li(p)=c) tn(i,c) =∑p1(Li(k)=c⋅^Li(p)≠c)

Then the overall mean IoU is the average of all C classes:

### 5.2 Per-frame based evaluation

Tracking information between consecutive frames is not available in the current release. Therefore, we use pre-frame based evaluation. However, rather than evaluating all the images together that is same as the evaluation for single image, we consider per-frame evaluation.

#### Metric for video semantic segmentation

We propose the per-frame IoU metric that evaluates each predicted frame independently.

Given a sequence of images with ground truth labels and predicted label . Let the metric between two corresponding images is Each predicted label will contain per-pixel prediction.

 F(S,S∗) =mean(∑im(Li,^Li)∑iNi) (2) m(Li,^Li) =[⋯,IoU(Li=j,^Li=j),⋯]T (3) Ni =[⋯,1(j∈L(Li) or j∈L(^Li),⋯] (4)

where IoU is computed between two binary masks and . means label is appeared in the ground truth label .

### 5.3 Metric for video object instance segmentation

We first match between ground truth and predicted instances based on the thresholding of overlapping areas. For each predicted instance, if the overlapping area between the predicted instance and the ignoring labels is larger than a threshold, the predicted instance is removed from the evaluation. Notice that the group classes, such as car group and bicycle group, are also ignored in the evaluation. Predicted instances that are not matched are counted as false positives.

We use the interpolated average precision (AP) [5] as the metric for object segmentation. The AP is computed for each class for all the image frames for each video clip. The mean AP (mAP) is then computed for all the video clips and all the classes.

## 6 Experiment Results for Image Parsing

We conducted our experiments on the Wide ResNet-38 network [13] that trades depth for width comparing with the original ResNet structure [6]. The released model is fine-tuned using our dataset with initial learning rate 0.0001, crop size , uniform sampling, 10 times data augmentation, and 100 epochs. The predictions are computed with one single scale 1.0 and without any post-processing steps. To be comparable with the training and testing in the ResNet-38 network, we select a small subset from our dataset that consists of 5,378 training images and 671 testing images, which are at the same order of fine labeled images in the Cityscapes dataset (i.e., around 5K training images and 500 test images). Table 5 shows the parsing results of classes in common for these two datasets. Notice that the IoUs computed based on our dataset are much lower than the IoUs from the Cityscapes. The mIoU for movable objects in our dataset is 34.6% lower than the one for the Cityscapes (common classes for both datasets).

## 7 Conclusion and Future Work

In this work, we present a large-scale comprehensive dataset of street views. This dataset contains 1) higher scene complexities than existing datasets; 2) 2D/3D annotations and pose information; 3) various annotated lane markings; 4) video frames with instance-level annotations.

In the future, we will first enlarge our dataset to achieve one million annotated video frames with more diversified conditions including snow, rain, and foggy environments. Second, we plan to mount stereo cameras and a panoramic camera system in near future to generate depth maps and panoramic images. In the current release, the depth information for the moving objects is still missing. We would like to produce complete depth information for both static background and moving objects.

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