Reconstruction of a 3D wireframe from a single line drawing via generative depth estimation

• URL: http://arxiv.org/abs/2604.13549v1
• Date: Wed, 15 Apr 2026 06:52:04 GMT
• Title: Reconstruction of a 3D wireframe from a single line drawing via generative depth estimation
• Abstract summary: Conversion of 2D freehand sketches into 3D models remains a pivotal challenge in computer vision.<n>We propose a generative approach by framing reconstruction as a conditional dense depth estimation task.<n>We train and evaluate our approach using a massive dataset of over one million image-depth pairs.
• Score: 5.596217605600402
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The conversion of 2D freehand sketches into 3D models remains a pivotal challenge in computer vision, bridging the gap between human creativity and digital fabrication. Traditional line drawing reconstruction relies on brittle symbolic logic, while modern approaches are constrained by rigid parametric modeling, limiting users to predefined CAD primitives. We propose a generative approach by framing reconstruction as a conditional dense depth estimation task. To achieve this, we implement a Latent Diffusion Model (LDM) with a ControlNet-style conditioning framework to resolve the inherent ambiguities of orthographic projections. To support an iterative "sketch-reconstruct-sketch" workflow, we introduce a graph-based BFS masking strategy to simulate partial depth cues. We train and evaluate our approach using a massive dataset of over one million image-depth pairs derived from the ABC Dataset. Our framework demonstrates robust performance across varying shape complexities, providing a scalable pipeline for converting sparse 2D line drawings into dense 3D representations, effectively allowing users to "draw in 3D" without the rigid constraints of traditional CAD.

Related papers

• CGGT: Curve-Grounded Geometry Transformer for 3D Parametric Curve Reconstruction [31.822849244645525]
  We propose a Curve-Grounded Geometry Transformer that grounds 3D-consistent 2D curve instances in the image space from sparse, unposed multi-view images.<n>We show that our framework achieves substantial improvements in both reconstruction accuracy and efficiency.<n>Despite being trained solely on synthetic data, CGGT generalizes well to real-world images.
  arXiv  Detail & Related papers  (2026-09-13T13:56:44Z)
• Towards Realistic and Consistent Orbital Video Generation via 3D Foundation Priors [61.34273238077091]
  We present a novel method for generating orbital videos from a single image of an object.<n>Our method achieves superior visual quality, shape realism and multi-view consistency compared to state-of-the-art methods.
  arXiv  Detail & Related papers  (2026-04-14T05:35:46Z)
• IGGT: Instance-Grounded Geometry Transformer for Semantic 3D Reconstruction [82.53307702809606]
  Humans naturally perceive the geometric structure and semantic content of a 3D world as intertwined dimensions.<n>We propose InstanceGrounded Geometry Transformer (IGGT) to unify the knowledge for both spatial reconstruction and instance-level contextual understanding.
  arXiv  Detail & Related papers  (2025-10-26T14:57:44Z)
• A Controllable 3D Deepfake Generation Framework with Gaussian Splatting [6.969908558294805]
  We propose a novel 3D deepfake generation framework based on 3D Gaussian Splatting.<n>It enables realistic, identity-preserving face swapping and reenactment in a fully controllable 3D space.<n>Our approach bridges the gap between 3D modeling and deepfake synthesis, enabling new directions for scene-aware, controllable, and immersive visual forgeries.
  arXiv  Detail & Related papers  (2025-09-15T06:34:17Z)
• From 2D CAD Drawings to 3D Parametric Models: A Vision-Language Approach [15.785592359384292]
  We present CAD2Program, a new method for reconstructing 3D parametric models from 2D CAD drawings.<n>We treat the 2D CAD drawing as an image, regardless of its original format, and encode the image with a standard ViT model.<n>On the output side, our method auto-regressively predicts a general-purpose language describing 3D parametric models in text form.
  arXiv  Detail & Related papers  (2024-12-16T15:41:14Z)
• GEOcc: Geometrically Enhanced 3D Occupancy Network with Implicit-Explicit Depth Fusion and Contextual Self-Supervision [49.839374549646884]
  This paper presents GEOcc, a Geometric-Enhanced Occupancy network tailored for vision-only surround-view perception.<n>Our approach achieves State-Of-The-Art performance on the Occ3D-nuScenes dataset with the least image resolution needed and the most weightless image backbone.
  arXiv  Detail & Related papers  (2024-05-17T07:31:20Z)
• Robust Geometry-Preserving Depth Estimation Using Differentiable Rendering [93.94371335579321]
  We propose a learning framework that trains models to predict geometry-preserving depth without requiring extra data or annotations. Comprehensive experiments underscore our framework's superior generalization capabilities. Our innovative loss functions empower the model to autonomously recover domain-specific scale-and-shift coefficients.
  arXiv  Detail & Related papers  (2023-09-18T12:36:39Z)
• FrozenRecon: Pose-free 3D Scene Reconstruction with Frozen Depth Models [67.96827539201071]
  We propose a novel test-time optimization approach for 3D scene reconstruction. Our method achieves state-of-the-art cross-dataset reconstruction on five zero-shot testing datasets.
  arXiv  Detail & Related papers  (2023-08-10T17:55:02Z)
• Unsupervised Style-based Explicit 3D Face Reconstruction from Single Image [10.1205208477163]
  In this work, we propose a general adversarial learning framework for solving Unsupervised 2D to Explicit 3D Style Transfer. Specifically, we merge two architectures: the unsupervised explicit 3D reconstruction network of Wu et al. and the Generative Adversarial Network (GAN) named StarGAN-v2. We show that our solution is able to outperform well established solutions such as DepthNet in 3D reconstruction and Pix2NeRF in conditional style transfer.
  arXiv  Detail & Related papers  (2023-04-24T21:25:06Z)
• SketchSampler: Sketch-based 3D Reconstruction via View-dependent Depth Sampling [75.957103837167]
  Reconstructing a 3D shape based on a single sketch image is challenging due to the large domain gap between a sparse, irregular sketch and a regular, dense 3D shape. Existing works try to employ the global feature extracted from sketch to directly predict the 3D coordinates, but they usually suffer from losing fine details that are not faithful to the input sketch.
  arXiv  Detail & Related papers  (2022-08-14T16:37:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.