Source-linked AI summary
Depth-aware CNN for RGB-D Segmentation
Weiyue Wang, Ulrich Neumann
TL;DR
CNNs’ fixed grid kernels limit geometric modeling, while existing RGB-D approaches can incur substantial computation or memory costs. Depth-aware CNN addresses this gap with depth-aware convolution and average pooling that propagate information according to depth similarity without added parameters. Experiments and ablations on RGB-D segmentation benchmarks support its effectiveness and flexibility.
Problem
CNNs have limited ability to model geometric information, and existing RGB-D methods using extra images, volumes, or point clouds can require high computation or memory.
Method
Depth-aware CNN adds depth similarity to convolution and pooling so geometry enters CNN information propagation without introducing parameters or computational complexity.
Results
Comparisons and extensive ablation studies on RGB-D semantic segmentation demonstrate Depth-aware CNN’s effectiveness and flexibility.
Takeaways & Limitations
Depth-aware CNN provides a general RGB-D framework that bridges 2D CNNs and 3D geometry while preserving CNN efficiency.
Takeaways & Limitations
The approach assumes that pixels with the same semantic label tend to have similar depths, an assumption supported by reported depth-variance observations.
Abstract
from arXiv · showhide
Convolutional neural networks (CNN) are limited by the lack of capability to handle geometric information due to the fixed grid kernel structure. The availability of depth data enables progress in RGB-D semantic segmentation with CNNs. State-of-the-art methods either use depth as additional images or process spatial information in 3D volumes or point clouds. These methods suffer from high computation and memory cost. To address these issues, we present Depth-aware CNN by introducing two intuitive, flexible and effective operations: depth-aware convolution and depth-aware average pooling. By leveraging depth similarity between pixels in the process of information propagation, geometry is seamlessly incorporated into CNN. Without introducing any additional parameters, both operators can be easily integrated into existing CNNs. Extensive experiments and ablation studies on challenging RGB-D semantic segmentation benchmarks validate the effectiveness and flexibility of our approach.
1 Introduction
Depth-aware CNN addresses CNNs’ difficulty modeling geometry by incorporating depth relations into convolution and pooling for RGB-D segmentation. Its operators preserve CNN efficiency while emphasizing pixels with similar depth.
- Motivation: CNNs struggle to incorporate geometric information because their fixed grid structure cannot use pixel relationships during information propagation.Two-stream RGB-D networks still retain this fixed geometric structure, while 3D alternatives increase computational or memory demands.
- Method: Depth-aware CNN introduces depth-aware convolution, which gives greater contribution to pixels whose depths resemble the receptive field center.The depth similarity term creates a depth-aware receptive field that is not constrained to fixed grid geometry.
- Method: Depth-aware average pooling incorporates pairwise depth relations when computing local means, allowing features to propagate along geometric structure and localize object boundaries.This complements depth-aware convolution by making pooling geometry-aware.
- Advantages: The two operators integrate depth geometry into CNNs without introducing parameters or computational complexity and can replace standard convolution and pooling.Their design is intended to preserve the efficiency and flexibility of conventional CNN architectures.
- Results: Comparisons with state-of-the-art methods and extensive RGB-D segmentation ablations support Depth-aware CNN’s flexibility, efficiency, and effectiveness.The framework is presented as connecting 2D CNNs with 3D geometry.
2 Related Works
Prior RGB-D segmentation methods either treat depth as an additional image or model geometry with volumetric and point-cloud representations. Depth-aware CNN instead integrates depth-defined geometric relations directly into standard CNN operations.
- RGB-D CNNs: Two-stream methods process RGB and depth separately, doubling network parameters and computation while retaining CNNs’ fixed geometric structure.HHA encoding is also used as a depth representation in separate network streams.
- 3D Representations: Volumetric CNNs model geometry but face high memory and computation costs that prevent scaling to larger settings.Point-cloud methods were introduced partly to address these volumetric limitations.
- 3D Representations: Point-cloud approaches can be more efficient than 3D CNNs, but k-nearest-neighbor operations still have high computational complexity.The cited discussion presents this as a remaining efficiency limitation.
- CNN Geometry: Dilated, spatial-transform, and deformable convolutions modify receptive fields or sampling locations, whereas Depth-aware CNN uses depth relations from RGB-D input.These related methods demonstrate that geometric transformations can improve vision-task performance.
- Depth-aware CNN: Depth-aware CNN uses a depth-defined weighted kernel to encode geometric relationships in convolution and pooling without adding parameters.The method links depth geometry to the spatial transformations handled by CNN operations.
3 Depth-aware CNN
Depth-aware CNN incorporates geometric relationships from depth images into convolution and pooling by weighting information propagation according to depth similarity. The resulting operations adapt receptive fields toward geometrically consistent regions while remaining compatible with standard CNNs.
- Depth-aware operations: Depth-aware CNN uses input feature maps and depth images to make convolution and pooling sensitive to geometric relationships.Both operations produce output feature maps while operating consistently across feature channels.
- Depth-aware convolution: Depth-aware convolution multiplies standard convolution contributions by a depth-similarity term, giving pixels with similar depths to the kernel center greater influence.It therefore uses both learnable convolution weights and depth-similarity weights; the similarity term does not introduce trainable parameters.
- Depth-aware average pooling: Depth-aware average pooling weights local feature values by pairwise depth similarity instead of treating every pixel equally.This prevents the fixed geometric structure of standard pooling and supports localization of object boundaries using depth.
- Effective receptive field: Depth-aware CNN adjusts effective receptive fields according to geometric variance, so sampled regions can mostly share the center pixel’s semantic label.The paper illustrates this behavior across three levels of 3 × 3 depth-aware convolutions.
- RGB-D semantic segmentation: The method replaces corresponding standard CNN operations in a DeepLab network with a modified VGG-16 encoder for RGB-D semantic segmentation.The architecture uses depth-aware operations while retaining DeepLab as the baseline framework.
4 Experiments
Experiments evaluate Depth-aware CNN on three RGB-D segmentation datasets against baselines and state-of-the-art methods, including ablations of its operators, similarity functions, runtime, and convergence.
- Main results: On NYUv2, D-CNN outperforms the baseline without additional parameters and exceeds the HHA network using half its parameters.The comparison trains networks from scratch and reports that D-CNN incorporates geometric information directly in convolution.
- Main results: On NYUv2, D-CNN is comparable with state-of-the-art methods, while D-CNN combined with HHA achieves state-of-the-art performance.These comparisons use networks initialized from pre-trained parameters; qualitative results are shown in Figure 4.
- Main results: On SUN-RGBD, D-CNN outperforms the baseline by a large margin, while adding HHA further improves performance and remains comparable with a state-of-the-art method that is more time-consuming.Baseline comparisons are trained from scratch, whereas state-of-the-art comparisons use pre-trained models.
- Main results: On SID, D-CNN achieves 4% IoU over CNN while preserving the same parameter count and computation complexity.Networks are trained from scratch for this comparison.
- Ablation and efficiency: Ablations show that adding depth-aware convolution to bottom VGG layers increases IoU by 6%, depth-aware pooling further promotes accuracy, and similarity variants remain above baseline and HHA.D-CNN also improves most object categories, especially ceilings and curtains, and converges faster than the baseline; its geometry integration adds no parameters.
5 Conclusion
The paper concludes that depth-aware convolution and depth-aware average pooling incorporate geometric variance into CNN operations while preserving efficiency and flexibility. It presents the framework as a bridge between 2D CNNs and 3D geometry, with future applications beyond RGB-D segmentation.
- 5 Conclusion: Depth-aware CNN encodes geometric variance through depth similarity in convolution and pooling without adding parameters or computational complexity.The operations adapt the effective receptive field while retaining CNN efficiency and can replace their plain counterparts.
- 5 Conclusion: Depth-aware CNN is a general RGB-D framework that bridges 2D CNNs and 3D geometry using raw depth images.Future work proposes applications to 3D detection, instance segmentation, more challenging datasets, and other 3D data such as LiDAR point clouds.