CSPN++: Learning Context and Resource Aware Convolutional Spatial Propagation Networks for Depth Completion (1911.05377v2)

Abstract: Depth Completion deals with the problem of converting a sparse depth map to a dense one, given the corresponding color image. Convolutional spatial propagation network (CSPN) is one of the state-of-the-art (SoTA) methods of depth completion, which recovers structural details of the scene. In this paper, we propose CSPN++, which further improves its effectiveness and efficiency by learning adaptive convolutional kernel sizes and the number of iterations for the propagation, thus the context and computational resources needed at each pixel could be dynamically assigned upon requests. Specifically, we formulate the learning of the two hyper-parameters as an architecture selection problem where various configurations of kernel sizes and numbers of iterations are first defined, and then a set of soft weighting parameters are trained to either properly assemble or select from the pre-defined configurations at each pixel. In our experiments, we find weighted assembling can lead to significant accuracy improvements, which we referred to as "context-aware CSPN", while weighted selection, "resource-aware CSPN" can reduce the computational resource significantly with similar or better accuracy. Besides, the resource needed for CSPN++ can be adjusted w.r.t. the computational budget automatically. Finally, to avoid the side effects of noise or inaccurate sparse depths, we embed a gated network inside CSPN++, which further improves the performance. We demonstrate the effectiveness of CSPN++on the KITTI depth completion benchmark, where it significantly improves over CSPN and other SoTA methods.

• The paper introduces CSPN++ with context-aware and resource-aware mechanisms that dynamically adjust convolutional configurations to improve depth accuracy.
• It employs a context-aware CSPN that adapts kernel sizes per pixel using weighted assembly, leading to significant gains in RMSE and MAE on the KITTI benchmark.
• The framework integrates a gated network for filtering noise and optimizing computational resources, achieving up to 5x faster processing for real-time applications.

An Expert Review of CSPN++: Enhancements in Depth Completion Tasks through Context and Resource Aware Convolutional Spatial Propagation Networks

The paper titled "CSPN++: Learning Context and Resource Aware Convolutional Spatial Propagation Networks for Depth Completion" substantially advances the state of the art in the domain of image-guided depth completion. This task, crucial for autonomous vehicles and robotic vision, involves converting sparse depth maps—typically obtained via LiDAR or stereo algorithms—into dense representations. The method CSPN (Convolutional Spatial Propagation Network) has been a prominent technique due to its accurate depth and structure preservation capabilities. This paper introduces CSPN++, a refined version of CSPN, enhancing both computational efficiency and accuracy through context and resource-awareness mechanisms.

Technical Contributions

The authors provide a novel approach to dynamic adaptive configuration via the introduction of two key strategies: context-aware CSPN (CA-CSPN) and resource-aware CSPN (RA-CSPN).

1. Context-Aware CSPN (CA-CSPN): This model variant employs a sophisticated architecture selection mechanism, whereby the convolutional kernel sizes and iterations are dynamically adapted per pixel. The integration of this spatially adaptive strategy is achieved through weighted assembling methods. Using a gradient-based optimization, CA-CSPN selects the optimal configurations for each pixel, significantly improving accuracy by better aligning depth with image structures and smoothing transitions.
2. Resource-Aware CSPN (RA-CSPN): Complementing the context-aware variant, RA-CSPN focuses on computational efficiency by allowing the model to minimize resource usage during inference. This is accomplished by training the model to predict kernel sizes and iteration numbers directly, based on computational budget constraints. RA-CSPN manages to accelerate processing significantly (by up to 5x as shown in experimental results) while maintaining similar accuracy levels to CA-CSPN, making it highly suited for resource-constrained environments.
3. Gated Network Integration: To address noise and errors in sparse depth maps, the authors incorporate a gated network inside the CSPN++ framework. This addition, crucial for depth consistency, filters erroneous measurements, ultimately augmenting the robustness of depth completion outputs.

Experimental Validation

The efficacy of CSPN++ is demonstrated using the KITTI benchmark for depth completion, wherein CSPN++ markedly surpasses CSPN and other competing methods in performance metrics such as RMSE and MAE. Specific figures highlight the reduction of RMSE errors and the improved quality of depth maps, showcasing substantial gains over previous techniques.

Implications and Future Directions

The improvements offered by CSPN++ hold significant implications for real-time systems like autonomous navigation where both accuracy and efficiency are critical. The context and resource-aware adaptations can be envisioned to extend beyond depth completion tasks, potentially being applicable to other domains like semantic segmentation and object detection, where dynamic resource allocation can yield performance benefits.

Future work may delve into extending these adaptive principles to other forms of spatial propagation networks. Further exploration into architectural optimizations that leverage neural architecture search (NAS) techniques could result in even more efficient models. Additionally, incorporating enhanced understanding of environmental context related to driving conditions could refine model predictions further.

In summary, CSPN++ effectively leverages adaptive convolutional configurations and computational resources, pushing forward the boundaries of current depth completion methodologies. This paper exemplifies how nuanced architectural modifications, underpinned by a robust experimental framework, can lead to both application-specific performance improvements and broader procedural insights.