Depth Estimation via Affinity Learned with Convolutional Spatial Propagation Network (1808.00150v1)

Abstract: Depth estimation from a single image is a fundamental problem in computer vision. In this paper, we propose a simple yet effective convolutional spatial propagation network (CSPN) to learn the affinity matrix for depth prediction. Specifically, we adopt an efficient linear propagation model, where the propagation is performed with a manner of recurrent convolutional operation, and the affinity among neighboring pixels is learned through a deep convolutional neural network (CNN). We apply the designed CSPN to two depth estimation tasks given a single image: (1) To refine the depth output from state-of-the-art (SOTA) existing methods; and (2) to convert sparse depth samples to a dense depth map by embedding the depth samples within the propagation procedure. The second task is inspired by the availability of LIDARs that provides sparse but accurate depth measurements. We experimented the proposed CSPN over two popular benchmarks for depth estimation, i.e. NYU v2 and KITTI, where we show that our proposed approach improves in not only quality (e.g., 30% more reduction in depth error), but also speed (e.g., 2 to 5 times faster) than prior SOTA methods.

• The paper presents a novel affinity learning mechanism using CSPN that achieves a 30% reduction in depth error compared to existing models.
• It refines state-of-the-art depth maps and transforms sparse LiDAR data into dense, smooth depth representations for robust perception in robotics and autonomous vehicles.
• The model processes data 2 to 5 times faster than prior methods, highlighting its potential for real-time applications and further scaling in computer vision tasks.

Depth Estimation via Affinity Learned with Convolutional Spatial Propagation Network

The paper presents a novel approach to depth estimation from a single image using a Convolutional Spatial Propagation Network (CSPN). This work addresses two core tasks of depth estimation: refining the depth output of existing state-of-the-art (SOTA) methods and converting sparse depth samples into a dense depth map. The motivation for the latter arises from the increasing availability of LiDAR data, which provides sparse yet accurate depth information.

Approach

The CSPN is designed as an efficient propagation model that learns the affinity matrix necessary for depth prediction. The authors implement a linear propagation model using a recurrent convolutional operation, where the affinity between neighboring pixels is learned through a deep Convolutional Neural Network (CNN). This involves a complex yet efficient transformation function within the local neighborhood of a pixel, enhanced iteratively to increase the context coverage.

For refining depth maps, CSPN serves two distinct functions: offering an improvement over existing depth estimation networks and acting as a transformation layer to embed sparse depth information into a dense format. The CSPN is capable of preserving the sparse depth values while ensuring smooth transitions in local contexts. This is particularly useful for robotics and autonomous vehicles where sparse LiDAR data needs to be integrated with imagery to form complete depth perceptions.

Numerical Results and Claims

The experimental analysis conducted on NYU v2 and KITTI datasets reflects the effectiveness of the CSPN. The results demonstrated significant improvement — a relative 30% reduction in depth error compared to previous models. CSPN's efficiency is highlighted further through its computational speed, achieving 2 to 5 times faster processing compared to prior methods like SPN. These results underscore the model's practical value in real-time applications.

Implications and Future Directions

The CSPN introduces a methodological advancement in depth estimation, showing potential for broader applications in fields requiring accurate depth perception. The approach is particularly crucial in autonomous systems relying on sparse sensor data for environment understanding. The successful integration of CSPN with sparse depth samples also paves the way for applications where noise-dominated or partially available sensor data is prevalent.

Looking forward, this research suggests potential developments in enhancing other vision tasks like image segmentation and enhancement via similar propagation networks. The model's efficient learning and propagation of affinity matrices highlight a robust framework adaptable to diverse neural network architectures. Further exploration into scalability and optimization of CSPN for varied tasks may yield a broader impact across computer vision applications.

In conclusion, the CSPN proposed in this paper establishes an important step towards more efficient and accurate depth estimation methodologies, with promising implications and future avenues for research and application in AI-driven perception systems.