3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation (1803.10409v1)

Abstract: We present 3DMV, a novel method for 3D semantic scene segmentation of RGB-D scans in indoor environments using a joint 3D-multi-view prediction network. In contrast to existing methods that either use geometry or RGB data as input for this task, we combine both data modalities in a joint, end-to-end network architecture. Rather than simply projecting color data into a volumetric grid and operating solely in 3D -- which would result in insufficient detail -- we first extract feature maps from associated RGB images. These features are then mapped into the volumetric feature grid of a 3D network using a differentiable backprojection layer. Since our target is 3D scanning scenarios with possibly many frames, we use a multi-view pooling approach in order to handle a varying number of RGB input views. This learned combination of RGB and geometric features with our joint 2D-3D architecture achieves significantly better results than existing baselines. For instance, our final result on the ScanNet 3D segmentation benchmark increases from 52.8\% to 75\% accuracy compared to existing volumetric architectures.

Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation

The paper "3DMV: Joint 3D-Multi-View Prediction for 3D Semantic Scene Segmentation" by Angela Dai and Matthias Nießner addresses the challenge of segmenting 3D scenes leveraging both geometric and color data. Traditional approaches primarily focus on either geometry or RGB-D data. This research seeks to integrate both modalities using a novel joint 3D-multi-view (3DMV) prediction network, thereby enhancing segmentation accuracy.

Network Architecture and Methodology

The core of 3DMV is its network architecture, which fuses 2D and 3D data to predict semantic labels for voxels within a 3D scene. The method deviates from the conventional practice of merely projecting color data into a 3D grid. Instead, it extracts detailed feature maps from 2D RGB images, which are then backprojected into a 3D volumetric grid using a differentiable layer. This process allows the network to retain the high-resolution features of the RGB input.

To manage the potentially large number of frames obtained from 3D scans, the research employs a multi-view pooling approach. This allows the network to seamlessly handle a variable number of views, ensuring flexibility and scalability, crucial for processing extensive indoor scenes.

Numerical Results and Contributions

The proposed 3DMV network shows a marked improvement over existing methods. The results on the ScanNet 3D segmentation benchmark highlight an increase in segmentation accuracy from 52.8% to 75%, illustrating the significant benefit of using combined geometric and RGB inputs. This improvement suggests that the joint 2D-3D architecture effectively exploits the complementary strengths of both data types.

An in-depth ablation paper further underscores the value of multi-view integration and end-to-end training. Notably, the inclusion of RGB data contributes substantially to accuracy gains, while geometry provides indispensable spatial context. The research demonstrates that using additional views, although yielding diminishing returns beyond a point, enhances performance due to better coverage.

Implications and Future Work

The results imply substantial potential benefits for robotics, where both semantic understanding and spatial awareness are vital. The increased accuracy in scene segmentation facilitated by 3DMV can support more sophisticated and reliable robot perception systems. Furthermore, the paper's findings could influence future research directions in combining multi-modal data for 3D vision tasks, especially in dense reconstruction scenarios.

Future work might explore more sophisticated data representations or leverage sparse techniques to handle high-resolution indoor scans efficiently. Additionally, extending such network architectures to perform semantic instance segmentation could broaden their applicability in complex environments. The integration of multi-modal features for reconstruction tasks, like scan completion and texture application, also presents a promising avenue for exploration.

This paper significantly contributes to the domain of 3D semantic segmentation by proposing a robust method that efficiently combines multiple data modalities to achieve high accuracy, thereby setting a precedent for subsequent advancements in this area.