SparseDC: Depth Completion from sparse and non-uniform inputs (2312.00097v1)

Abstract: We propose SparseDC, a model for Depth Completion of Sparse and non-uniform depth inputs. Unlike previous methods focusing on completing fixed distributions on benchmark datasets (e.g., NYU with 500 points, KITTI with 64 lines), SparseDC is specifically designed to handle depth maps with poor quality in real usage. The key contributions of SparseDC are two-fold. First, we design a simple strategy, called SFFM, to improve the robustness under sparse input by explicitly filling the unstable depth features with stable image features. Second, we propose a two-branch feature embedder to predict both the precise local geometry of regions with available depth values and accurate structures in regions with no depth. The key of the embedder is an uncertainty-based fusion module called UFFM to balance the local and long-term information extracted by CNNs and ViTs. Extensive indoor and outdoor experiments demonstrate the robustness of our framework when facing sparse and non-uniform input depths. The pre-trained model and code are available at https://github.com/WHU-USI3DV/SparseDC.

• The paper introduces a Sparse Feature Filling Module (SFFM) to stabilize sparse depth inputs using image features as proxies.
• It employs a dual-branch CNN/ViT architecture with an Uncertainty-based Fusion Module (UFFM) to integrate local detail and global context.
• Experimental results on NYU, KITTI, and SUN RGB-D demonstrate a 17% boost in REL and a 7.8% reduction in RMSE over preceding methods.

SparseDC: Advancements in Depth Completion from Sparse and Non-Uniform Inputs

The paper "SparseDC: Depth Completion from Sparse and Non-Uniform Inputs" provides an in-depth exploration into the complex task of depth completion, addressing challenges that primarily arise from the variability in input depth data, which can be sparse and non-uniform. SparseDC is a proposed solution that builds on the limitations of extant methods which have typically been evaluated on benchmark datasets with fixed data distributions. By expressly targeting these non-uniform inputs, SparseDC contributes a novel perspective and methodology to the field of depth completion, which holds significance for practical applications like autonomous driving and 3D reconstruction.

Core Contributions

The contribution of SparseDC is two-fold. Firstly, the introduction of a Sparse Feature Filling Module (SFFM) addresses the instability inherent in sparse inputs by utilizing image features as stable proxies to fill missing data. Secondly, a two-branch feature embedder, integrating CNNs and ViTs, is developed to predict different characteristics of depth information. Specifically, the embedder employs an Uncertainty-based Fusion Module (UFFM) that harmoniously balances local, detailed information extracted by CNNs and the global context captured by ViTs. This dual approach adeptly manages the complex task of depth completion in varying input conditions.

Methodological Innovations

SparseDC is distinct in its methodology. The SFFM improves upon feature robustness by using image features to compensate for sparse depth data, effectively stabilizing the input feature space. The two-branch architecture, a pivotal component of SparseDC, leverages the strengths of CNNs and ViTs to process local geometrical details and global structures, respectively. The UFFM within this architecture predicts and rectifies pixel-wise uncertainty, enhancing the feature fusion process and leading to a more refined depth completion output.

Experimental Validation

The experimental evaluation of SparseDC is comprehensive and underscores its effectiveness. Conducted on both indoor and outdoor datasets, specifically NYU Depth V2, KITTI, and SUN RGB-D, the results demonstrate significant performance improvements across multiple metrics, notably RMSE and REL, over leading methods such as NLSPN and CompletionFormer. With an improvement of approximately 17% on the REL metric and 7.8% on the RMSE metric over CompletionFormer, SparseDC establishes its robustness, especially in datasets characterized by sparse and non-uniform inputs.

Implications and Future Outlook

The practical implications of this research are significant, particularly in fields that require reliable depth estimation from potentially challenging data inputs, like autonomous vehicles and augmented reality systems. Theoretically, the approach promotes a shift in focus towards methods that can dynamically adapt to input variability. For future research, building upon the uncertainty-based methodologies and leveraging advances in hybrid architectures could further refine depth completion processes, making systems more resilient to diverse real-world conditions.

In essence, the contribution of SparseDC lies in its capacity to handle variability in sparse and non-uniform depth data inputs, providing a novel framework that combines robustness and efficiency. As applications in computer vision and edge computing continue to evolve, methodologies like SparseDC will be crucial for developing advanced systems capable of seamless, accurate environmental understanding.