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angeladai/cnncomplete

0
View on GitHub↗
142 stars·25 forks·C++·11 viewsgraphics.stanford.edu/projects/cnncomplete↗

Cnncomplete

This repo contains code to train a volumetric deep neural network to complete partially scanned 3D shapes. More information can be found in our paper .

Features

  • 3D Reconstruction - Shape completion using encoder-predictor CNN architectures.
  • Shape Completion - Shape completion using encoder-predictor CNNs.

Star history

Star history chart for angeladai/cnncompleteStar history chart for angeladai/cnncomplete

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

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Projects sharing features with Cnncomplete

These projects share indexed features with Cnncomplete. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
  • colmap/colmapcolmap avatar

    colmap/colmap

    12,014View on GitHub↗

    COLMAP is a 3D scene reconstruction suite and C++ geometry library that implements a full structure-from-motion pipeline. It functions as a GPU-accelerated photogrammetry tool and multi-view stereo framework designed to produce dense 3D geometry and watertight meshes from collections of 2D images. The project distinguishes itself through hardware-accelerated feature extraction and a modular camera modeling system that supports perspective, fisheye, and equirectangular lens types. It employs vocabulary tree image retrieval to efficiently identify similar images in large datasets and provides P

    C++
    View on GitHub↗12,014
  • microsoft/trellis.2microsoft avatar

    microsoft/TRELLIS.2

    3,910View on GitHub↗

    TRELLIS.2 is a generative image-to-3D system that creates high-resolution 3D assets with physically based rendering materials from 2D images. It utilizes a sparse voxel representation to handle complex topologies and internal structures without relying on iso-surface fields. The project features a structured latent space representation that maps geometry and texture attributes to maintain visual fidelity. It employs an optimization-free geometry reconstruction process to decode latent representations directly into voxel grids and includes a PBR texture generator for synthesizing base color, r

    Python
    View on GitHub↗3,910
  • ashawkey/stable-dreamfusionashawkey avatar

    ashawkey/stable-dreamfusion

    8,841View on GitHub↗

    This project is a diffusion-based 3D generator and image-to-3D reconstruction system. It translates natural language descriptions or two-dimensional images into three-dimensional assets using neural radiance fields and diffusion models. The system utilizes score-distillation sampling and diffusion-based guidance to refine 3D shapes without requiring 3D training data. It includes specialized tools for transforming neural representations into exportable meshes with texture and material data, as well as a pipeline for iterative optimization of geometry and textures. The project covers a broad r

    Python
    View on GitHub↗8,841
  • mrforexample/comfyui-3d-packMrForExample avatar

    MrForExample/ComfyUI-3D-Pack

    3,648View on GitHub↗

    ComfyUI-3D-Pack is a suite of custom nodes for ComfyUI that enables 3D asset generation and rendering within a node-based workflow. It provides a set of tools for reconstructing textured three-dimensional meshes and volumetric scenes from single images, multi-view images, or text prompts. The system includes a Gaussian splatting generator for creating high-fidelity volumetric 3D scene representations and a multi-view image generator to produce consistent image sets for reconstruction. It also features a single image 3D mesh tool to build geometry from a single 2D source. The toolset covers 3

    Pythoncomfycomfyuimachine-learning
    View on GitHub↗3,648
Compare all 30 related projects→

Frequently asked questions

What does angeladai/cnncomplete do?

This repo contains code to train a volumetric deep neural network to complete partially scanned 3D shapes. More information can be found in our paper .

What are the main features of angeladai/cnncomplete?

The main features of angeladai/cnncomplete are: 3D Reconstruction, Shape Completion.

Which projects share features with angeladai/cnncomplete?

Projects with overlapping indexed features include: colmap/colmap — COLMAP is a 3D scene reconstruction suite and C++ geometry library that implements a full structure-from-motion… tencentarc/instantmesh — InstantMesh is a neural 3D reconstruction tool and single-image 3D mesh generator. It utilizes a sparse-view large… mrforexample/comfyui-3d-pack — ComfyUI-3D-Pack is a suite of custom nodes for ComfyUI that enables 3D asset generation and rendering within a… microsoft/trellis.2 — TRELLIS.2 is a generative image-to-3D system that creates high-resolution 3D assets with physically based rendering… ashawkey/stable-dreamfusion — This project is a diffusion-based 3D generator and image-to-3D reconstruction system. It translates natural language… gaoxiang12/slambook — Slambook is a visual SLAM framework designed for simultaneous localization and mapping. It provides an integrated…