Frustum PointNets for 3D Object Detection from RGB-D Data

TL;DR

This paper introduces Frustum PointNets, a novel method for 3D object detection from RGB-D data that directly operates on raw point clouds, combining 2D detection and 3D deep learning for high accuracy and efficiency.

Contribution

The paper presents a new approach that integrates 2D object detection with 3D deep learning on raw point clouds, improving detection accuracy and speed over previous voxel-based methods.

Findings

Outperforms state-of-the-art on KITTI and SUN RGB-D benchmarks.

Achieves real-time 3D detection with high recall.

Handles occlusion and sparse data effectively.

Abstract

In this work, we study 3D object detection from RGB-D data in both indoor and outdoor scenes. While previous methods focus on images or 3D voxels, often obscuring natural 3D patterns and invariances of 3D data, we directly operate on raw point clouds by popping up RGB-D scans. However, a key challenge of this approach is how to efficiently localize objects in point clouds of large-scale scenes (region proposal). Instead of solely relying on 3D proposals, our method leverages both mature 2D object detectors and advanced 3D deep learning for object localization, achieving efficiency as well as high recall for even small objects. Benefited from learning directly in raw point clouds, our method is also able to precisely estimate 3D bounding boxes even under strong occlusion or with very sparse points. Evaluated on KITTI and SUN RGB-D 3D detection benchmarks, our method outperforms the state of the art by remarkable margins while having real-time capability.