홈 › Papers

DepthLab: From Partial to Complete

2024-12-24 · Zhiheng Liu, Ka Leong Cheng, Qiuyu Wang, Shuzhe Wang, Hao Ouyang, Bin Tan, Kai Zhu, Yujun Shen, Qifeng Chen, Ping Luo

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/.

📄 PDF Abstract BibTeX arXiv:2412.18153

Code (0)

등록된 구현이 없습니다.

Tasks

Depth CompletionMissing ValuesScene Generation Text to 3D

Methods 이 논문이 사용한 방법론

Diffusion Diffusion models generate samples by gradually removing noise from a signal, and their training objective can be expressed as a reweighted variational lower-bound…

Inpainting Train a convolutional neural network to generate the contents of an arbitrary image region conditioned on its surroundings.

DepthLab: Real-Time 3D Interaction With Depth Maps for Mobile Augmented Reality

2020-10-22 · Ruofei Du, Eric Turner, Maksym Dzitsiuk, Luca Prasso 외

Real-time depth data is readily available on mobile phones with passive or active sensors and on VR/AR devices. However, this rich data about our environment is under-explored in mainstream AR applications. Slow adoption…

Depth And Camera Motion Depth Estimation Indoor Monocular Depth Estimation Monocular Depth Estimation

Panoptic-Depth Color Map for Combination of Depth and Image Segmentation

2023-08-24 · Jia-Quan Yu, Soo-Chang Pei

Image segmentation and depth estimation are crucial tasks in computer vision, especially in autonomous driving scenarios. Although these tasks are typically addressed separately, we propose an innovative approach to comb…

Autonomous Driving Depth Estimation Image Segmentation Segmentation+1

P2C: Self-Supervised Point Cloud Completion from Single Partial Clouds

2023-07-27 · ICCV 2023 1 · Ruikai Cui, Shi Qiu, Saeed Anwar, Jiawei Liu 외

Point cloud completion aims to recover the complete shape based on a partial observation. Existing methods require either complete point clouds or multiple partial observations of the same object for learning. In contras…

Point Cloud Completion

Incomplete Graph Representation and Learning via Partial Graph Neural Networks

2020-03-23 · Bo Jiang, Ziyan Zhang

Graph Neural Networks (GNNs) are gaining increasing attention on graph data learning tasks in recent years. However, in many applications, graph may be coming in an incomplete form where attributes of graph nodes are par…

Attribute

Learning a Structured Latent Space for Unsupervised Point Cloud Completion

2022-03-29 · CVPR 2022 1 · Yingjie Cai, Kwan-Yee Lin, Chao Zhang, Qiang Wang 외

Unsupervised point cloud completion aims at estimating the corresponding complete point cloud of a partial point cloud in an unpaired manner. It is a crucial but challenging problem since there is no paired partial-compl…