Semantic Scene Completion from a Single Depth Image (1611.08974v1)

Abstract: This paper focuses on semantic scene completion, a task for producing a complete 3D voxel representation of volumetric occupancy and semantic labels for a scene from a single-view depth map observation. Previous work has considered scene completion and semantic labeling of depth maps separately. However, we observe that these two problems are tightly intertwined. To leverage the coupled nature of these two tasks, we introduce the semantic scene completion network (SSCNet), an end-to-end 3D convolutional network that takes a single depth image as input and simultaneously outputs occupancy and semantic labels for all voxels in the camera view frustum. Our network uses a dilation-based 3D context module to efficiently expand the receptive field and enable 3D context learning. To train our network, we construct SUNCG - a manually created large-scale dataset of synthetic 3D scenes with dense volumetric annotations. Our experiments demonstrate that the joint model outperforms methods addressing each task in isolation and outperforms alternative approaches on the semantic scene completion task.

Overview of "SSCNet for Simultaneous Scene Completion and Semantic Labeling"

The paper "SSCNet for Simultaneous Scene Completion and Semantic Labeling" presents an approach to enhance the semantic understanding and geometrical completion of 3D scenes from a single-view depth map. The primary contribution is a novel neural network, SSCNet, which simultaneously predicts volumetric occupancy and semantic labels for each voxel, improving performance on both tasks by leveraging their inherent interdependence.

Core Contributions

1. SSCNet Architecture: The SSCNet uses an end-to-end 3D convolutional network designed to handle the sparsity and contextual requirements of 3D data. It includes a dilation-based 3D context module that expands the receptive field necessary for capturing long-range spatial contexts, enhancing the accuracy of both scene completion and semantic labeling.
2. SUNCG Dataset: The authors introduce a large-scale synthetic dataset called SUNCG, consisting of over 45,622 indoor scenes with manually created dense volumetric annotations. This dataset addresses the scarcity of ground truth data for training deep neural networks in 3D scene understanding.
3. Flipped TSDF Encoding: The network input is encoded using a modified Truncated Signed Distance Function (TSDF) to mitigate view-dependence and spatial resolution issues. This flipped TSDF accumulates high-gradient data near surfaces, aiding the network in learning meaningful geometric features.
4. Multi-Scale Context Aggregation: SSCNet aggregates multi-scale features to address the diverse physical sizes of objects, allowing better capture of both local and global contextual information.

Numerical Results and Claims

The authors substantiate their claims with rigorous quantitative comparisons against current methodologies. Particularly, SSCNet achieves superior performance on the semantic scene completion task compared to isolated methods and alternative scene completion approaches. For example, on the NYU dataset, SSCNet achieves an Intersection over Union (IoU) score of 56.6% for semantic scene completion when pre-trained on the SUNCG dataset and fine-tuned on NYU data. Additionally, the network demonstrates a notable improvement in voxel accuracy over methods like those proposed by Geiger and Wang (IoU of 19.6%) and Lin et al. (IoU of 12.0%).

Theoretical and Practical Implications

The implications of this research are multifaceted:

1. Enhanced Scene Understanding: By jointly addressing scene completion and semantic labeling, SSCNet demonstrates that understanding the semantic context of objects aids in predicting their geometry, and vice-versa. This presents a paradigm shift from tackling these problems in isolation to a more holistic approach.
2. Synthetic Data Utilization: The SUNCG dataset proves invaluable, depicting that synthetic data, when plentiful and diverse, can significantly augment real-world datasets, enhancing the generalizability and robustness of 3D learning models.
3. Efficient Context Utilization: The application of a dilation-based context module effectively expands the receptive field without exponential growth in computational costs. This reflects an efficient way to capitalize on the often-underutilized 3D contextual information in sparse data environments.

Future Developments

Future research directions might explore:

1. Integration of RGB Data: Incorporating RGB data alongside depth maps could potentially enhance the semantic understanding, especially for objects indistinguishable by depth alone, such as planar surfaces with unique textures.
2. Real-time Applications: Given SSCNet's architecture designed for efficient context learning, optimizing and deploying this model for real-time applications in robotics and augmented reality may be the next logical step.
3. Higher Resolution Outputs: Addressing the resolution constraints to handle finer geometrical details and smaller objects may further enhance the model’s applicability in complex environments.

In summary, the SSCNet presents a significant advancement in the joint task of scene completion and semantic labeling, providing valuable insights and methodologies which pave the way for improved 3D scene understanding and interaction in computational vision systems.