Search Results for author:
Found 6 papers, 4 papers with code
RGBD2: Generative Scene Synthesis via Incremental View Inpainting using RGBD Diffusion Models
More specifically, we maintain an intermediate surface mesh used for rendering new RGBD views, which subsequently becomes complete by an inpainting network; each rendered RGBD view is later back-projected as a partial surface and is supplemented into the intermediate mesh.
TANGO: Text-driven Photorealistic and Robust 3D Stylization via Lighting Decomposition
Creation of 3D content by stylization is a promising yet challenging problem in computer vision and graphics research.
Learning and Meshing from Deep Implicit Surface Networks Using an Efficient Implementation of Analytic Marching
More specifically, we identify from the linear regions, partitioned by an MLP based implicit function, the analytic cells and analytic faces that are associated with the function's zero-level isosurface.
SA-ConvONet: Sign-Agnostic Optimization of Convolutional Occupancy Networks
To this end, we propose to learn implicit surface reconstruction by sign-agnostic optimization of convolutional occupancy networks, to simultaneously achieve advanced scalability to large-scale scenes, generality to novel shapes, and applicability to raw scans in a unified framework.
Sign-Agnostic Implicit Learning of Surface Self-Similarities for Shape Modeling and Reconstruction from Raw Point Clouds
Motivated from a universal phenomenon that self-similar shape patterns of local surface patches repeat across the entire surface of an object, we aim to push forward the data-driven strategies and propose to learn a local implicit surface network for a shared, adaptive modeling of the entire surface for a direct surface reconstruction from raw point cloud; we also enhance the leveraging of surface self-similarities by improving correlations among the optimized latent codes of individual surface patches.
Analytic Marching: An Analytic Meshing Solution from Deep Implicit Surface Networks
This paper studies a problem of learning surface mesh via implicit functions in an emerging field of deep learning surface reconstruction, where implicit functions are popularly implemented as multi-layer perceptrons (MLPs) with rectified linear units (ReLU).
