3D-SIS: 3D Semantic Instance Segmentation of RGB-D Scans

TL;DR

3D-SIS is a neural network that combines geometric and color information from multi-view RGB-D scans to improve 3D semantic instance segmentation accuracy, outperforming existing methods on benchmark datasets.

Contribution

The paper introduces a novel multi-modal neural network architecture that fuses 2D image features with 3D volumetric data for enhanced 3D instance segmentation.

Findings

Achieves over 13 mAP improvement on real-world benchmarks.

Effectively fuses multi-view RGB-D data for better segmentation.

Outperforms state-of-the-art methods in accuracy.

Abstract

We introduce 3D-SIS, a novel neural network architecture for 3D semantic instance segmentation in commodity RGB-D scans. The core idea of our method is to jointly learn from both geometric and color signal, thus enabling accurate instance predictions. Rather than operate solely on 2D frames, we observe that most computer vision applications have multi-view RGB-D input available, which we leverage to construct an approach for 3D instance segmentation that effectively fuses together these multi-modal inputs. Our network leverages high-resolution RGB input by associating 2D images with the volumetric grid based on the pose alignment of the 3D reconstruction. For each image, we first extract 2D features for each pixel with a series of 2D convolutions; we then backproject the resulting feature vector to the associated voxel in the 3D grid. This combination of 2D and 3D feature learning allows significantly higher accuracy object detection and instance segmentation than state-of-the-art alternatives. We show results on both synthetic and real-world public benchmarks, achieving an improvement in mAP of over 13 on real-world data.