DepthLab: From Partial to Complete
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/.
676b7d07d886f8125a4fb855
Zhiheng Liu
676b7d07d886f8125a4fb856
Ka Leong Cheng
676b7d07d886f8125a4fb857
Qiuyu Wang
676b7d07d886f8125a4fb858
Shuzhe Wang
676b7d07d886f8125a4fb859
Hao Ouyang
676b7d07d886f8125a4fb85a
Bin Tan
676b7d07d886f8125a4fb85b
Kai Zhu
676b7d07d886f8125a4fb85c
Yujun Shen
676b7d07d886f8125a4fb85d
Qifeng Chen
676b7d07d886f8125a4fb85e
Ping Luo
DepthLab: From Partial to Complete
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/.
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.
depth inpainting image diffusion priors 3D scene inpainting text-to-3D scene generation sparse-view reconstruction LiDAR depth completion