Dense 3D reconstruction from RGB images traditionally assumes static camera pose estimates. This assumption has endured, even as recent works have increasingly focused on real-time methods for mobile devices. However, the assumption of a fixed pose for each image does not hold for online execution: poses from real-time SLAM are dynamic and may be updated following events such as bundle adjustment and loop closure. This has been addressed in the RGB-D setting, by de-integrating past views and re-integrating them with updated poses, but it remains largely untreated in the RGB-only setting. We formalize this problem to define the new task of dense online reconstruction from dynamically-posed images. To support further research, we introduce a dataset called LivePose1containing the dynamic poses from a SLAM system running on Scan-Net [6]. We select three recent reconstruction systems and apply a framework based on de-integration to adapt each one to the dynamic-pose setting. In addition, we propose a novel, non-linear de-integration module that learns to remove stale scene content. We show that responding to pose updates is critical for high-quality reconstruction, and that our de-integration framework is an effective solution.

Abstract Reconstructing surfaces from scanned 3D points has been an important research area for several decades. One common approach that has proven efficient and robust to noise is implicit surface reconstruction, i.e. fitting to the points a 3D scalar function (such as an indicator function or signed‐distance field) and then extracting an isosurface. Though many techniques fall within this category, existing methods either impose no boundary constraints or impose Dirichlet/Neumann conditions on the surface of a bounding box containing the scanned data. In this work, we demonstrate the benefit of supporting Dirichlet constraints on a general boundary. To this end, we adapt the Screened Poisson Reconstruction algorithm to input a constraint envelope in addition to the oriented point cloud. We impose Dirichlet boundary conditions, forcing the reconstructed implicit function to be zero outside this constraint surface. Using a visual hull and/or depth hull derived from RGB‐D scans to define the constraint envelope, we obtain substantially improved surface reconstructions in regions of missing data.

The commoditization of virtual and augmented reality devices and the availability of inexpensive consumer depth cameras have catalyzed a resurgence of interest in spatiotemporal performance capture. Recent systems like Fusion4D and Holoportation address several crucial problems in the real-time fusion of multiview depth maps into volumetric and deformable representations. Nonetheless, stitching multiview video textures onto dynamic meshes remains challenging due to imprecise geometries, occlusion seams, and critical time constraints. In this paper, we present a practical solution towards real-time seamless texture montage for dynamic multiview reconstruction. We build on the ideas of dilated depth discontinuities and majority voting from Holoportation to reduce ghosting effects when blending textures. In contrast to their approach, we determine the appropriate blend of textures per vertex using view-dependent rendering techniques, so as to avert fuzziness caused by the ubiquitous normal-weighted blending. By leveraging geodesics-guided diffusion and temporal texture fields, our algorithm mitigates spatial occlusion seams while preserving temporal consistency. Experiments demonstrate significant enhancement in rendering quality, especially in detailed regions such as faces. We envision a wide range of applications for Montage4D, including immersive telepresence for business, training, and live entertainment.

Processing signals on surfaces often involves resampling the signal over the vertices of a dense mesh and applying mesh-based filtering operators. We present a framework to process a signal directly in a texture atlas domain. The benefits are twofold: avoiding resampling degradation and exploiting the regularity of the texture image grid. The main challenges are to preserve continuity across atlas chart boundaries and to adapt differential operators to the non-uniform parameterization. We introduce a novel function space and multigrid solver that jointly enable robust, interactive, and geometry-aware signal processing. We demonstrate our approach using several applications including smoothing and sharpening, multiview stitching, geodesic distance computation, and line integral convolution.

We convert a sequence of unstructured textured meshes into a mesh with incrementally changing connectivity and atlas parameterization. Like prior work on surface tracking, we seek temporally coherent mesh connectivity to enable efficient representation of surface geometry and texture. Like recent work on evolving meshes, we pursue local remeshing to permit tracking over long sequences containing significant deformations or topological changes. Our main contribution is to show that both goals are realizable within a common framework that simultaneously evolves both the set of mesh triangles and the parametric map. Sparsifying the remeshing operations allows the formation of large spatiotemporal texture charts. These charts are packed as prisms into a 3D atlas for a texture video. Reducing tracking drift using mesh-based optical flow helps improve compression of the resulting video stream.

Scanned performances are commonly represented in virtual environments as sequences of textured triangle meshes. Detailed shapes deforming over time benefit from meshes with dynamically evolving connectivity. We analyze these unstructured mesh sequences to automatically synthesize motion graphs with new smooth transitions between compatible poses and actions. Such motion graphs enable natural periodic motions, stochastic playback, and user-directed animations. The main challenge of unstructured sequences is that the meshes differ not only in connectivity but also in alignment, shape, and texture. We introduce new geometry processing techniques to address these problems and demonstrate visually seamless transitions on high-quality captures.

We present the first end-to-end solution to create high-quality free-viewpoint video encoded as a compact data stream. Our system records performances using a dense set of RGB and IR video cameras, generates dynamic textured surfaces, and compresses these to a streamable 3D video format. Four technical advances contribute to high fidelity and robustness: multimodal multi-view stereo fusing RGB, IR, and silhouette information; adaptive meshing guided by automatic detection of perceptually salient areas; mesh tracking to create temporally coherent subsequences; and encoding of tracked textured meshes as an MPEG video stream. Quantitative experiments demonstrate geometric accuracy, texture fidelity, and encoding efficiency. We release several datasets with calibrated inputs and processed results to foster future research.

Abstract In this paper, we present a novel approach for efficiently evolving meshes using mean‐curvature flow. We use a finite‐elements hierarchy that supports an efficient multigrid solver for performing the semi‐implicit time‐stepping. Although expensive to compute, we show that it is possible to track this hierarchy through the process of surface evolution. As a result, we provide a way to efficiently flow the surface through the evolution, without requiring a costly initialization at the beginning of each time‐step. Using our approach, we demonstrate a factor of nearly seven‐fold improvement over the non‐tracking implementation, supporting the evolution of surfaces consisting of 1M triangles at a rate of just a few seconds per update.

We present a general framework for performing geometry filtering through the solution of a screened Poisson equation. We show that this framework can be efficiently adapted to a changing Riemannian metric to support curvature-aware filtering and describe a parallel and streaming multigrid implementation for solving the system. We demonstrate the practicality of our approach by developing an interactive system for mesh editing that allows for exploration of a large family of curvature-guided, anisotropic filters.

We describe a real-time system for editing large 3D meshes. The system supports both global and local modulation of surface detail, expressing the position of vertices on the edited geometry as the solution to a linear system of equations defined over the surface. The interactivity of our system is enabled by the design of an efficient sparse linear solver, providing an interface with which users can explore a broad landscape of possible surface modifications.

Abstract We present a novel approach for computing and solving the Poisson equation over the surface of a mesh. As in previous approaches, we define the Laplace‐Beltrami operator by considering the derivatives of functions defined on the mesh. However, in this work, we explore a choice of functions that is decoupled from the tessellation. Specifically, we use basis functions (second‐order tensor‐product B‐splines) defined over 3D space, and then restrict them to the surface. We show that in addition to being invariant to mesh topology, this definition of the Laplace‐Beltrami operator allows a natural multiresolution structure on the function space that is independent of the mesh structure, enabling the use of a simple multigrid implementation for solving the Poisson equation.