DepthLab: From Partial to Complete (2412.18153v1)

Abstract: Missing values remain a common challenge for depth data across its wide range of applications, stemming from various causes like incomplete data acquisition and perspective alteration. This work bridges this gap with DepthLab, a foundation depth inpainting model powered by image diffusion priors. Our model features two notable strengths: (1) it demonstrates resilience to depth-deficient regions, providing reliable completion for both continuous areas and isolated points, and (2) it faithfully preserves scale consistency with the conditioned known depth when filling in missing values. Drawing on these advantages, our approach proves its worth in various downstream tasks, including 3D scene inpainting, text-to-3D scene generation, sparse-view reconstruction with DUST3R, and LiDAR depth completion, exceeding current solutions in both numerical performance and visual quality. Our project page with source code is available at https://johanan528.github.io/depthlab_web/.

• The paper presents a dual-branch diffusion framework that integrates RGB and depth data for geometrically consistent inpainting.
• It employs a Reference U-Net for extracting RGB features and an Estimation U-Net for processing noisy and masked depths, enhancing robustness in challenging regions.
• Experimental results on datasets like NYUv2, KITTI, and others demonstrate state-of-the-art performance, even in zero-shot scenarios.

An Analysis of "DepthLab: From Partial to Complete"

The paper "DepthLab: From Partial to Complete" presents an innovative approach to the complex problem of depth inpainting in computer vision. Depth inpainting, defined as the task of reconstructing missing or occluded depth information in images, has significant implications across various domains such as robotics, augmented reality, and 3D vision. This paper introduces a novel methodology that utilizes a foundation depth inpainting model powered by image diffusion priors.

Methodological Insights

The authors propose a dual-branch diffusion-based framework, incorporating a Reference U-Net and an Estimation U-Net. The Reference U-Net is tasked with extracting RGB features from a conditional image, while the Estimation U-Net processes the noisy depth, the masked depth, and a binary inpainting mask. Importantly, the model leverages the power of image diffusion priors, which facilitates the handling of complex scenes and considerable masked regions. This model design enhances resilience to depth-deficient regions and ensures scale consistency with the known depth.

The innovative aspect of this approach is its capacity to integrate both image and depth features into a cohesive framework allowing for depth estimates that are geometrically consistent with known regions. The process involves random scale normalization during training, which addresses the challenge of handling local, non-global extrema of known depth within the latent representation space. Such architectural nuances grant this model significant adaptability and robustness across various conditions and tasks.

Performance Evaluation

This paper's experimental evaluation demonstrates impressive results across several benchmarks, including NYUv2, KITTI, ETH3D, ScanNet, and DIODE datasets. According to the data provided, DepthLab notably outperforms both discriminative and generative depth completion methods—even under zero-shot conditions. The model achieves state-of-the-art performance metrics in terms of absolute relative error (AbsRel) and accuracy within thresholds (δ1) across the evaluated datasets.

Furthermore, the authors address the applicability of their approach in several downstream tasks, such as 3D scene inpainting, text-to-3D scene generation, sparse-view reconstruction using DUST3R, and LiDAR depth completion. These applications highlight the model's versatility and effectiveness in extending beyond traditional depth estimation tasks into diverse, real-world scenarios.

Implications and Future Directions

From a theoretical standpoint, the approach represents a significant step forward in the integration of RGB and depth data for image-based depth completion. The fusion of diffusion models with multi-source data inputs offers a pathway for more robust and comprehensive interpretations of complex scene information. Practically, the DepthLab framework provides a potent tool for developers in fields such as autonomous navigation, virtual environment creation, and any other context where understanding three-dimensional space from limited data is crucial.

In terms of future research, expansions could include optimizing the speed of inference using flow matching-based models or enhancing the VAE's ability to encode sparse input. Another promising direction could involve experimenting with alternative conditioning techniques in U-Net architectures to better preserve sparse and fine-grained input details. Building upon such improvements could further enhance the model's capability and usability across more applications.

In conclusion, this paper presents a sophisticated integration of depth and RGB data through an innovative application of diffusion models, achieving state-of-the-art results in depth inpainting and demonstrating significant potential for diverse applications in three-dimensional data-intensive domains.