mlsanityData to 7 Oct 2026

Spatiotemporal Texture Reconstruction for Dynamic Objects Using a Single RGB-D Camera

arXiv 2108.09007 ↗First posted 20 Aug 2021cs.CV cs.GRcited by 3

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Abstract (arXiv)

This paper presents an effective method for generating a spatiotemporal (time-varying) texture map for a dynamic object using a single RGB-D camera. The input of our framework is a 3D template model and an RGB-D image sequence. Since there are invisible areas of the object at a frame in a single-camera setup, textures of such areas need to be borrowed from other frames. We formulate the problem as an MRF optimization and define cost functions to reconstruct a plausible spatiotemporal texture for a dynamic object. Experimental results demonstrate that our spatiotemporal textures can reproduce the active appearances of captured objects better than approaches using a single texture map.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 7 of its 51 references that are in the map. First posted on arXiv 20 Aug 2021.

Builds on

7 of 51 references.

References · 7

  1. 2019PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitizationcited by 818
  2. 2016BundleFusion: Real-time Globally Consistent 3D Reconstruction using On-the-fly Surface Re-integrationcited by 116
  3. 2018DoubleFusion: Real-time Capture of Human Performances with Inner Body Shapes from a Single Depth Sensorcited by 95
  4. 2016VolumeDeform: Real-time Volumetric Non-rigid Reconstructioncited by 75
  5. 2020Adversarial Texture Optimization from RGB-D Scanscited by 30
  6. 2020Quasi-Newton Solver for Robust Non-Rigid Registrationcited by 17
  7. 2019Volumetric Capture of Humans with a Single RGBD Camera via Semi-Parametric Learningcited by 13

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Cited by year

Citing papers per year: 2021: 1; 2022: 1; 2023: 1.

3 papers.