Center-based

The main contribution of CenterPoint lies in its adoption of a center-based detection head, which simplifies the process of dealing with object positions by not considering their intrinsic orientations. This significantly reduces the search space of the object detector, especially beneficial when vehicles drive on straight roads. Traditional anchor-based methods struggle to fit axis-aligned bounding boxes to rotated objects during critical maneuvers like a left turn.

Method

CenterPoint proposes representing, detecting, and tracking 3D objects as points. By regressing to 3D bounding boxes directly at the center point without voting, this method uses a single positive cell for each object alongside a keypoint estimation loss. A two-stage 3D detector with a Lidar-based backbone network identifies the centers of objects and their attributes, with a second stage refining all estimates.

Input

3D object detection aims to predict three-dimensional rotated bounding boxes. CenterPoint directly regresses to 3D bounding boxes through features at the center point without voting. This method employs a single positive cell for each object and utilizes a keypoint estimation loss. The two-stage detector extracts sparse features of 5 surface center points from the intermediate feature map.

Center-based Detection Head

Initially, six tasks are generated in the code, with the nuScenes dataset comprising 10 categories. Four tasks have two categories each. The separation of regression for ‘car’ and ‘pedestrian’ is due to the relatively large difference in size, allowing both to achieve higher accuracy more easily. Grouping ‘pedestrian’ and ‘traffic cone’ in the same task is because their sizes from a Bird’s Eye View (BEV) are similar, which can avoid generating two different categories of targets at the same position. Every task has the same head, and each head has a similar structure, including predictions for offset, height, dimensions, rotation angle, velocity, and a heatmap center.

Loss

• Regression loss for dimensions, offset, height, rotation: The loss is calculated based on the differences between the predicted annotation boxes and the target boxes. The localization loss sums up all these component losses, and the total loss is a weighted sum of heatmap loss and localization loss.

3D Object Tracking

Many 2D tracking algorithms can directly track 3D objects out of the box. However, dedicated 3D trackers based on 3D Kalman filters still have an advantage as they better exploit the three-dimensional motion in a scene.