3D-SIS: 3D Semantic Instance Segmentation of RGB-D Scans (1812.07003v3)

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.

• The paper introduces an innovative approach that fuses 2D RGB features with 3D geometry for improved instance segmentation.
• The methodology employs a dual-backbone network with 3D region proposals and ROI pooling to achieve a 13.5 mAP performance gain.
• The model is trained on both synthetic and real-world datasets, demonstrating enhanced segmentation accuracy in complete 3D scenes.

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

The paper introduces "3D-SIS," an innovative neural network architecture for 3D semantic instance segmentation in RGB-D scans, which provides significant advances over existing methodologies. This approach uniquely incorporates both color and geometric data, enabling enhanced instance segmentation accuracy. The paper demonstrates robust improvements in performance on both synthetic and real-world datasets, showcasing the potential applications in various computer vision tasks.

Methodology Overview

3D-SIS leverages the integration of multi-view RGB-D input data to construct a comprehensive 3D semantic instance segmentation approach. It combines high-resolution 2D RGB features with 3D scan geometry features, utilizing a fully-convolutional network architecture capable of processing entire 3D environments efficiently.

Core Components

1. Data Fusion: The method exploits multi-modal data, associating 2D features from RGB images with a 3D volumetric grid aligned with the 3D reconstruction. This backprojection technique allows the blending of 2D and 3D features, which is crucial for improving detection fidelity.
2. Network Architecture:
   • Utilizes ResNet blocks and 3D convolutions to learn semantic features.
   • A novel 3D Region Proposal Network (3D-RPN) and 3D Region of Interest (3D-RoI) pooling layer are used to infer object bounding boxes, class labels, and per-voxel instance masks.
   • Integrates a two-backbone system for detection and mask prediction, enhancing the training convergence and segmentation accuracy.
3. Training and Implementation:
   • The model is trained on synthetic and real-world datasets like SUNCG and ScanNetV2.
   • Features are extracted and trained in chunks, allowing the end-to-end learning process to generalize to entire scenes.

Results and Performance

The research exhibits that 3D-SIS significantly outperforms existing methodologies such as Mask R-CNN and SGPN, achieving a remarkable 13.5 mAP improvement on real-world data. This performance leap is attributed to the combined learning from both RGB and geometry signals, and the capability to process full 3D scenes seamlessly, leading to higher consistency and accuracy in object recognition.

Implications and Future Work

The approach sets a new standard for 3D semantic instance segmentation, with substantial implications in practical applications. The methodological advancements may influence sectors like autonomous vehicles, AR/VR, and robotics where understanding spatial relationships in complex environments is crucial.

Theoretical and Practical Contribution

3D-SIS fills a critical gap in current computer vision solutions by effectively combining 2D and 3D features in a unified framework. It extends beyond traditional sensor fusion techniques, providing a comprehensive, end-to-end trainable model that addresses the limitations of existing single-frame methods.

Speculation on Future Developments

Future work might explore the scalability of this approach to larger and more complex data environments, potentially utilizing advanced techniques like transfer learning to enhance adaptability. The integration with more sophisticated SLAM systems could further optimize feature alignment, thus improving spatial awareness and prediction accuracy.

In conclusion, 3D-SIS represents a significant step forward in 3D semantic instance segmentation, offering new opportunities for research and application in the rapidly evolving field of computer vision. The paper’s insights into multi-modal learning present a compelling case for further exploration and adaptation in real-world scenarios.