Indoor Semantic Segmentation using depth information (1301.3572v2)

Abstract: This work addresses multi-class segmentation of indoor scenes with RGB-D inputs. While this area of research has gained much attention recently, most works still rely on hand-crafted features. In contrast, we apply a multiscale convolutional network to learn features directly from the images and the depth information. We obtain state-of-the-art on the NYU-v2 depth dataset with an accuracy of 64.5%. We illustrate the labeling of indoor scenes in videos sequences that could be processed in real-time using appropriate hardware such as an FPGA.

• The paper introduces an innovative multiscale ConvNet that learns hierarchical features by fusing RGB and depth data.
• It achieves a notable 64.5% pixelwise accuracy on the NYU Depth dataset, outperforming traditional hand-crafted feature methods.
• The approach offers practical advantages in robotics and augmented reality by utilizing depth information to improve scene labeling.

Indoor Semantic Segmentation Using Depth Information

The paper focuses on the integration of depth information in the semantic segmentation of indoor scenes using RGB-D inputs, presenting a meaningful advancement in the domain of computer vision. The approach employs a multiscale convolutional network (ConvNet) to learn features directly from input images, leading to significant improvements over traditional methods that largely rely on hand-crafted features.

Core Contributions and Methodology

The introduction of the innovative multiscale ConvNet for feature learning marks a notable development. The ConvNet processes both RGB and depth data, allowing for the learning of hierarchical features across multiple scales. This is accomplished through a pipeline where color (RGB) channels and depth images are transformed using a Laplacian pyramid. The ConvNet operates on these multiscale representations, generating robust features for scene labeling.

A significant highlight is the attention given to the NYU Depth Dataset (v2), which contains diverse indoor scenes enriched with depth information. The dataset's sheer size and complexity, with hundreds of video sequences and 407024 frames, provide an extensive testbed for the proposed approach.

Numerical Results

The authors report obtaining state-of-the-art results on the NYU-v2 depth dataset with a pixelwise accuracy of 64.5%. Furthermore, notable performance gains were observed in recognizing specific classes such as 'floor,' 'furniture,' and 'ceiling,' all benefitting from the additional depth cues. The improved accuracy underscores the efficacy of incorporating depth information into the feature learning process.

Theoretical and Practical Implications

The use of depth information within a feature learning framework opens up new avenues for research and application. Practically, this approach is advantageous in real-time scenarios, like robotics and augmented reality, where depth data helps disambiguate objects that are challenging to identify using only RGB data. The paper also hints at potential applications in support inference between objects, suggesting future utility in automation and smart environments.

Future Directions

While the paper successfully demonstrates the integration of depth in ConvNets, it suggests opportunities for further exploration, such as the use of more sophisticated data augmentation strategies and the potential extension of training datasets. Other promising avenues include the use of unsupervised feature learning and graph-based segmentation methods to refine predictions.

In conclusion, the work represents a substantial step in the advancement of feature learning for semantic segmentation. By leveraging both RGB and depth data, it sets the stage for more nuanced and accurate scene understanding in indoor environments.