3D-SIS: 3D Semantic Instance Segmentation of RGB-D Scans
This addresses the problem of accurate object detection and instance segmentation in 3D environments for computer vision applications, representing a strong specific gain.
The paper tackles 3D semantic instance segmentation in RGB-D scans by introducing 3D-SIS, a neural network that jointly learns from geometric and color signals, achieving an improvement in mAP of over 13 on real-world data.
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.