title: DepthLab: From Partial to Complete
summary: 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/.
paper_authors._id: 676b7d07d886f8125a4fb855
paper_authors.name: Zhiheng Liu
paper_authors._id: 676b7d07d886f8125a4fb856
paper_authors.name: Ka Leong Cheng
paper_authors._id: 676b7d07d886f8125a4fb857
paper_authors.name: Qiuyu Wang
paper_authors._id: 676b7d07d886f8125a4fb858
paper_authors.name: Shuzhe Wang
paper_authors._id: 676b7d07d886f8125a4fb859
paper_authors.name: Hao Ouyang
paper_authors._id: 676b7d07d886f8125a4fb85a
paper_authors.name: Bin Tan
paper_authors._id: 676b7d07d886f8125a4fb85b
paper_authors.name: Kai Zhu
paper_authors._id: 676b7d07d886f8125a4fb85c
paper_authors.name: Yujun Shen
paper_authors._id: 676b7d07d886f8125a4fb85d
paper_authors.name: Qifeng Chen
paper_authors._id: 676b7d07d886f8125a4fb85e
paper_authors.name: Ping Luo
paper_title: DepthLab: From Partial to Complete
paper_summary: 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/.
paper_ai_summary: DepthLab, a depth inpainting model utilizing image diffusion priors, effectively handles missing depth data, preserving scale consistency and excelling in tasks like 3D scene inpainting and LiDAR depth completion.
paper_ai_keywords: depth inpainting image diffusion priors 3D scene inpainting text-to-3D scene generation sparse-view reconstruction LiDAR depth completion