# VolumeDeform: Real-time Volumetric Non-rigid Reconstruction

arXiv: https://arxiv.org/abs/1603.08161 · HTML: https://mlsanity.com/paper/1603.08161/

## Key facts

- Cited by 75 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Mar 2016.

## Built on by (most cited first, 30 of 75)

- [Shape of Motion: 4D Reconstruction from a Single Video](https://mlsanity.com/paper/2407.13764/) (2024)
- [DynIBaR: Neural Dynamic Image-Based Rendering](https://mlsanity.com/paper/2211.11082/) (2022)
- [BundleSDF: Neural 6-DoF Tracking and 3D Reconstruction of Unknown Objects](https://mlsanity.com/paper/2303.14158/) (2023)
- [Kimera: from SLAM to Spatial Perception with 3D Dynamic Scene Graphs](https://mlsanity.com/paper/2101.06894/) (2021)
- [4DComplete: Non-Rigid Motion Estimation Beyond the Observable Surface](https://mlsanity.com/paper/2105.01905/) (2021)
- [DeVRF: Fast Deformable Voxel Radiance Fields for Dynamic Scenes](https://mlsanity.com/paper/2205.15723/) (2022)
- [Lepard: Learning partial point cloud matching in rigid and deformable scenes](https://mlsanity.com/paper/2111.12591/) (2021)
- [Geo4D: Leveraging Video Generators for Geometric 4D Scene Reconstruction](https://mlsanity.com/paper/2504.07961/) (2025)
- [Weakly Supervised Learning of Rigid 3D Scene Flow](https://mlsanity.com/paper/2102.08945/) (2021)
- [A Survey of Non-Rigid 3D Registration](https://mlsanity.com/paper/2203.07858/) (2022)
- [Neural Surface Reconstruction of Dynamic Scenes with Monocular RGB-D Camera](https://mlsanity.com/paper/2206.15258/) (2022)
- [Consistent Depth of Moving Objects in Video](https://mlsanity.com/paper/2108.01166/) (2021)
- [Self-supervised Neural Articulated Shape and Appearance Models](https://mlsanity.com/paper/2205.08525/) (2022)
- [Dynamic Surface Function Networks for Clothed Human Bodies](https://mlsanity.com/paper/2104.03978/) (2021)
- [NeuralHOFusion: Neural Volumetric Rendering under Human-object Interactions](https://mlsanity.com/paper/2202.12825/) (2022)
- [GarmentNets: Category-Level Pose Estimation for Garments via Canonical Space Shape Completion](https://mlsanity.com/paper/2104.05177/) (2021)
- [GlORIE-SLAM: Globally Optimized RGB-only Implicit Encoding Point Cloud SLAM](https://mlsanity.com/paper/2403.19549/) (2024)
- [HOSNeRF: Dynamic Human-Object-Scene Neural Radiance Fields from a Single Video](https://mlsanity.com/paper/2304.12281/) (2023)
- [Robust Dual Gaussian Splatting for Immersive Human-centric Volumetric Videos](https://mlsanity.com/paper/2409.08353/) (2024)
- [Recent Trends in 3D Reconstruction of General Non-Rigid Scenes](https://mlsanity.com/paper/2403.15064/) (2024)
- [VGGT4D: Mining Motion Cues in Visual Geometry Transformers for 4D Scene Reconstruction](https://mlsanity.com/paper/2511.19971/) (2025)
- [OcclusionFusion: Occlusion-aware Motion Estimation for Real-time Dynamic 3D Reconstruction](https://mlsanity.com/paper/2203.07977/) (2022)
- [ChallenCap: Monocular 3D Capture of Challenging Human Performances using Multi-Modal References](https://mlsanity.com/paper/2103.06747/) (2021)
- [RobustFusion: Robust Volumetric Performance Reconstruction under Human-object Interactions from Monocular RGBD Stream](https://mlsanity.com/paper/2104.14837/) (2021)
- [Temporally Consistent Online Depth Estimation in Dynamic Scenes](https://mlsanity.com/paper/2111.09337/) (2021)
- [AvatarCap: Animatable Avatar Conditioned Monocular Human Volumetric Capture](https://mlsanity.com/paper/2207.02031/) (2022)
- [Instant-NVR: Instant Neural Volumetric Rendering for Human-object Interactions from Monocular RGBD Stream](https://mlsanity.com/paper/2304.03184/) (2023)
- [POSEFusion: Pose-guided Selective Fusion for Single-view Human Volumetric Capture](https://mlsanity.com/paper/2103.15331/) (2021)
- [VoteHMR: Occlusion-Aware Voting Network for Robust 3D Human Mesh Recovery from Partial Point Clouds](https://mlsanity.com/paper/2110.08729/) (2021)
- [Decaf: Monocular Deformation Capture for Face and Hand Interactions](https://mlsanity.com/paper/2309.16670/) (2023)

## Abstract (arXiv)

We present a novel approach for the reconstruction of dynamic geometric shapes using a single hand-held consumer-grade RGB-D sensor at real-time rates. Our method does not require a pre-defined shape template to start with and builds up the scene model from scratch during the scanning process. Geometry and motion are parameterized in a unified manner by a volumetric representation that encodes a distance field of the surface geometry as well as the non-rigid space deformation. Motion tracking is based on a set of extracted sparse color features in combination with a dense depth-based constraint formulation. This enables accurate tracking and drastically reduces drift inherent to standard model-to-depth alignment. We cast finding the optimal deformation of space as a non-linear regularized variational optimization problem by enforcing local smoothness and proximity to the input constraints. The problem is tackled in real-time at the camera's capture rate using a data-parallel flip-flop optimization strategy. Our results demonstrate robust tracking even for fast motion and scenes that lack geometric features.