Deep Sliding Shapes for Amodal 3D Object Detection in RGB-D Images

TL;DR

This paper introduces Deep Sliding Shapes, a 3D ConvNet approach for amodal 3D object detection in RGB-D images, featuring a novel 3D RPN and joint recognition network that significantly improves accuracy and speed.

Contribution

It presents the first 3D RPN and joint recognition network for amodal detection, advancing 3D object detection in RGB-D images with improved performance and efficiency.

Findings

Outperforms previous methods by 13.8 mAP

Achieves 200x faster detection speed

Effectively handles objects of various sizes

Abstract

We focus on the task of amodal 3D object detection in RGB-D images, which aims to produce a 3D bounding box of an object in metric form at its full extent. We introduce Deep Sliding Shapes, a 3D ConvNet formulation that takes a 3D volumetric scene from a RGB-D image as input and outputs 3D object bounding boxes. In our approach, we propose the first 3D Region Proposal Network (RPN) to learn objectness from geometric shapes and the first joint Object Recognition Network (ORN) to extract geometric features in 3D and color features in 2D. In particular, we handle objects of various sizes by training an amodal RPN at two different scales and an ORN to regress 3D bounding boxes. Experiments show that our algorithm outperforms the state-of-the-art by 13.8 in mAP and is 200x faster than the original Sliding Shapes. All source code and pre-trained models will be available at GitHub.