Efficiency increased by 16 times! VRSO: 3D annotation of purely visual static objects, opening up the data closed loop!

The Sorrow of Annotation

Static object detection (SOD), including traffic lights, guide signs and traffic cones, most algorithms are data-driven deep neural networks and require a large amount of training data. Current practice typically involves manual annotation of large numbers of training samples on LiDAR-scanned point cloud data to fix long-tail cases.

Manual annotation is difficult to capture the variability and complexity of real scenes, and often fails to take into account occlusions, different lighting conditions and diverse viewing angles (yellow arrow in Figure 1) . The whole process has long links, is extremely time-consuming, error-prone, and costly (Figure 2). So currently companies are looking for automatic labeling solutions, especially based on pure vision. After all, not every car has lidar.

VRSO is a vision-based annotation system for static object annotation. It mainly uses information from SFM, 2D object detection and instance segmentation results. Overall effect: The average projection error of

• annotations is only 2.6 pixels, which is about a quarter of Waymo annotations (10.6 pixels)
• Compared with manual annotation, the speed is improved About 16 times

For static objects, VRSO solves the challenge of integrating and deduplicating static objects from different viewing angles through instance segmentation and contour extraction of key points, as well as the difficulty of insufficient observation due to occlusion issues , thus improving the accuracy of labeling. From Figure 1, compared with the manual annotation results of the Waymo Open dataset, VRSO demonstrates higher robustness and geometric accuracy.

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How to break the situation

The VRSO system is mainly divided into two parts: Scene reconstruction and Static objects are marked .

#The reconstruction part is not the focus, it is based on the SFM algorithm to restore the image pose and sparse 3D key points.

Static object annotation algorithm, combined with pseudo code, the general process is (the following will be detailed step by step):

• Use ready-made 2D object detection and segmentation algorithms to generate candidates
• Use the 3D-2D keypoint correspondence in the SFM model to track 2D instances across frames
• Introduce reprojection consistency to optimize the 3D annotation parameters of static objects

1. Tracking association

• #step 1: Extract 3D points within the 3D bounding box based on the key points of the SFM model.
• step 2: Calculate the coordinates of each 3D point on the 2D map based on the 2D-3D matching relationship.
• step 3: Determine the corresponding instance of the 3D point on the current 2D map based on the 2D map coordinates and instance segmentation corner points.
• step 4: Determine the correspondence between 2D observations and 3D bounding boxes for each 2D image.

2.proposal generation

Initialize the 3D frame parameters (position, direction, size) of the static object for the entire video clip. Each key point of SFM has an accurate 3D position and corresponding 2D image. For each 2D instance, feature points within the 2D instance mask are extracted. Then, a set of corresponding 3D keypoints can be considered as candidates for 3D bounding boxes.

Street signs are represented as rectangles with directions in space, which have 6 degrees of freedom, including translation (,,), direction (θ) and size (width and height). Given its depth, a traffic light has 7 degrees of freedom. Traffic cones are represented similarly to traffic lights.

3.proposal refine

• step 1: Extract the outline of each static object from 2D instance segmentation.
• step 2: Fit the minimum oriented bounding box (OBB) for the contour outline.
• step 3: Extract the vertices of the minimum bounding box.
• step 4: Calculate the direction based on the vertices and center points, and determine the vertex order.
• step 5: The segmentation and merging process is performed based on the 2D detection and instance segmentation results.
• step 6: Detect and reject observations containing occlusions. Extracting vertices from the 2D instance segmentation mask requires that all four corners of each sign are visible. If there are occlusions, axis-aligned bounding boxes (AABBs) are extracted from the instance segmentation and the area ratio between AABBs and 2D detection boxes is calculated. If there are no occlusions, these two area calculation methods should be close.

4. Triangulation

Obtain the initial vertex value of the static object under 3D conditions through triangulation.

By checking the number of keypoints in the 3D bounding boxes obtained by SFM and instance segmentation during scene reconstruction, only instances with the number of keypoints exceeding the threshold are considered stable and valid observations. For these instances, the corresponding 2D bounding box is considered a valid observation. Through 2D observation of multiple images, the vertices of the 2D bounding box are triangulated to obtain the coordinates of the bounding box.

For circular signs that do not distinguish the "lower left, upper left, upper right, upper right, and lower right" vertices on the mask, these circular signs need to be identified. 2D detection results are used as observations of circular objects, and 2D instance segmentation masks are used for contour extraction. The center point and radius are calculated through a least squares fitting algorithm. The parameters of the circular sign include the center point (,,), direction (θ) and radius ().

5.tracking refine

Tracking feature point matching based on SFM. Determine whether to merge these separated instances based on the Euclidean distance of the 3D bounding box vertices and the 2D bounding box projection IoU. Once merging is complete, 3D feature points within an instance can be clustered to associate more 2D feature points. Iterative 2D-3D association is performed until no 2D feature points can be added.

6. Final parameter optimization

Taking the rectangular sign as an example, the parameters that can be optimized include position (,,) and direction (θ) and size (,), for a total of six degrees of freedom. The main steps include:

• Convert six degrees of freedom into four 3D points and calculate the rotation matrix.
• Project the converted four 3D points onto the 2D image.
• Calculate the residual between the projection result and the corner point result obtained by instance segmentation.
• Use Huber to optimize and update bounding box parameters

Labeling effect

There are also some challenging long-tail cases, such as extremely low resolution and insufficient lighting.

To summarize

The VRSO framework achieves high-precision and consistent 3D annotation of static objects, tightly integrating detection, segmentation and The SFM algorithm eliminates manual intervention in intelligent driving annotation and provides results comparable to LiDAR-based manual annotation. Qualitative and quantitative evaluations were conducted with the widely recognized Waymo Open Dataset: compared with manual annotation, the speed is increased by about 16 times, while maintaining the best consistency and accuracy.

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