IPOD: Intensive Point-based Object Detector for Point Cloud

TL;DR

IPOD is a novel 3D object detection framework that leverages raw point cloud data with point-based proposals, achieving high recall and state-of-the-art performance on KITTI dataset.

Contribution

The paper introduces an end-to-end trainable point-based detection architecture that improves 3D object detection accuracy from raw point clouds.

Findings

Achieves state-of-the-art results on KITTI dataset.

Demonstrates high recall and fidelity in point cloud processing.

Excels particularly on challenging detection scenarios.

Abstract

We present a novel 3D object detection framework, named IPOD, based on raw point cloud. It seeds object proposal for each point, which is the basic element. This paradigm provides us with high recall and high fidelity of information, leading to a suitable way to process point cloud data. We design an end-to-end trainable architecture, where features of all points within a proposal are extracted from the backbone network and achieve a proposal feature for final bounding inference. These features with both context information and precise point cloud coordinates yield improved performance. We conduct experiments on KITTI dataset, evaluating our performance in terms of 3D object detection, Bird's Eye View (BEV) detection and 2D object detection. Our method accomplishes new state-of-the-art , showing great advantage on the hard set.