# Objects as Points

## TL;DR

This paper introduces CenterNet, a novel object detection method that models objects as points, leading to a simpler, faster, and more accurate detector that achieves state-of-the-art speed-accuracy trade-offs on multiple datasets.

## Contribution

The paper proposes a point-based object detection approach using keypoint estimation, simplifying the detection pipeline and improving speed and accuracy over traditional bounding box methods.

## Key findings

- CenterNet achieves 28.1% AP at 142 FPS on MS COCO.
- It outperforms many multi-stage detectors in speed and accuracy.
- The method is effective for 3D bounding box estimation and human pose detection.

## Abstract

Detection identifies objects as axis-aligned boxes in an image. Most successful object detectors enumerate a nearly exhaustive list of potential object locations and classify each. This is wasteful, inefficient, and requires additional post-processing. In this paper, we take a different approach. We model an object as a single point --- the center point of its bounding box. Our detector uses keypoint estimation to find center points and regresses to all other object properties, such as size, 3D location, orientation, and even pose. Our center point based approach, CenterNet, is end-to-end differentiable, simpler, faster, and more accurate than corresponding bounding box based detectors. CenterNet achieves the best speed-accuracy trade-off on the MS COCO dataset, with 28.1% AP at 142 FPS, 37.4% AP at 52 FPS, and 45.1% AP with multi-scale testing at 1.4 FPS. We use the same approach to estimate 3D bounding box in the KITTI benchmark and human pose on the COCO keypoint dataset. Our method performs competitively with sophisticated multi-stage methods and runs in real-time.