Axolotl3D: a Unified Framework for Faithful 3D Shape Completion

• URL: http://arxiv.org/abs/2607.20660v1
• Date: Wed, 22 Jul 2026 18:38:20 GMT
• Title: Axolotl3D: a Unified Framework for Faithful 3D Shape Completion
• Abstract summary: We present Axolotl3D, a 3D generation model that conditions on images, visibility masks, camera parameters, and a partial point cloud.<n>A unified training strategy synthesizes diverse conditioning regimes from large-scale 3D data, enabling robust cross-modal reasoning.<n>Experiments on Toys4K and OmniObject3D demonstrate state-of-the-art performance under both clean and occluded settings.
• Score: 1.6934548577260198
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent 3D generative models produce high-quality geometry from a single image using large-scale priors and diffusion architectures. However, they assume complete visibility and single-view inputs, limiting applicability in multi-view, occluded, or editing scenarios. Although prior works address these challenges individually, they lack a unified framework for controllable 3D completion under diverse conditioning signals. We present Axolotl3D, a multi-modal and occlusion-aware 3D generation model that jointly conditions on images, visibility masks, camera parameters, and a partial point cloud. The point cloud serves as a geometric anchor promoting faithful shape completion, while camera parameters ensure consistent multi-view alignment in a shared 3D coordinate system. A unified training strategy synthesizes diverse conditioning regimes from large-scale 3D data, enabling robust cross-modal reasoning. Experiments on Toys4K and OmniObject3D demonstrate state-of-the-art performance under both clean and occluded settings, as well as strong results in real-world reconstruction and geometry-consistent editing.

Related papers

• 3DCarGen: Scalable 3D Car Generation via 3D-consistent Multi-view Synthesis [35.43901502343984]
  3DCarGen is a scalable single-view 3D car generation framework for real-world images.<n>Our approach achieves robust geometric consistency and reconstruction fidelity compared to existing methods.
  arXiv  Detail & Related papers  (2026-06-23T07:44:14Z)
• 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)
• OneWorld: Taming Scene Generation with 3D Unified Representation Autoencoder [90.8453349494245]
  We present OneWorld, a framework that performs diffusion directly within a coherent 3D representation space.<n>OneWorld generates high-quality 3D scenes with superior cross-view consistency compared to state-of-the-art 2D-based methods.
  arXiv  Detail & Related papers  (2026-03-17T03:43:37Z)
• ViewMorpher3D: A 3D-aware Diffusion Framework for Multi-Camera Novel View Synthesis in Autonomous Driving [20.935790354765604]
  We introduce ViewMorpher3D, a multi-view image enhancement framework based on image diffusion models.<n>Unlike single-view approaches, ViewMorpher3D jointly processes a set of rendered views conditioned on camera poses, 3D geometric priors, and temporally adjacent or spatially overlapping reference views.<n>Our framework accommodates variable numbers of cameras and flexible reference/target view configurations, making it adaptable to diverse sensor setups.
  arXiv  Detail & Related papers  (2026-01-12T13:44:14Z)
• WorldMirror: Universal 3D World Reconstruction with Any-Prior Prompting [51.69408870574092]
  We present WorldMirror, an all-in-one, feed-forward model for versatile 3D geometric prediction tasks.<n>Our framework flexibly integrates diverse geometric priors, including camera poses, intrinsics, and depth maps.<n>WorldMirror achieves state-of-the-art performance across diverse benchmarks from camera, point map, depth, and surface normal estimation to novel view synthesis.
  arXiv  Detail & Related papers  (2025-10-12T17:59:09Z)
• UniLat3D: Geometry-Appearance Unified Latents for Single-Stage 3D Generation [98.40254523605581]
  UniLat3D is a unified framework that encodes geometry and appearance in a single latent space.<n>Our key contribution is a geometry-appearance Unified VAE, which compresses high-resolution sparse features into a compact latent representation.<n>UniLat3D produces high-quality 3D assets in seconds from a single image.
  arXiv  Detail & Related papers  (2025-09-29T17:21:23Z)
• Constructing a 3D Scene from a Single Image [31.11317559252235]
  SceneFuse-3D is a training-free framework designed to synthesize coherent 3D scenes from a single top-down view.<n>We decompose the input image into overlapping regions and generate each using a pretrained 3D object generator.<n>This modular design allows us to overcome resolution bottlenecks and preserve spatial structure without requiring 3D supervision or fine-tuning.
  arXiv  Detail & Related papers  (2025-05-21T17:10:47Z)
• SYM3D: Learning Symmetric Triplanes for Better 3D-Awareness of GANs [5.84660008137615]
  SYM3D is a novel 3D-aware GAN designed to leverage the prevalental symmetry structure found in natural and man-made objects. We demonstrate its superior performance in capturing detailed geometry and texture, even when trained on only single-view images.
  arXiv  Detail & Related papers  (2024-06-10T16:24:07Z)
• Unique3D: High-Quality and Efficient 3D Mesh Generation from a Single Image [28.759158325097093]
  Unique3D is a novel image-to-3D framework for efficiently generating high-quality 3D meshes from single-view images. Our framework features state-of-the-art generation fidelity and strong generalizability.
  arXiv  Detail & Related papers  (2024-05-30T17:59:54Z)
• LAM3D: Large Image-Point-Cloud Alignment Model for 3D Reconstruction from Single Image [64.94932577552458]
  Large Reconstruction Models have made significant strides in the realm of automated 3D content generation from single or multiple input images. Despite their success, these models often produce 3D meshes with geometric inaccuracies, stemming from the inherent challenges of deducing 3D shapes solely from image data. We introduce a novel framework, the Large Image and Point Cloud Alignment Model (LAM3D), which utilizes 3D point cloud data to enhance the fidelity of generated 3D meshes.
  arXiv  Detail & Related papers  (2024-05-24T15:09:12Z)
• CC3D: Layout-Conditioned Generation of Compositional 3D Scenes [49.281006972028194]
  We introduce CC3D, a conditional generative model that synthesizes complex 3D scenes conditioned on 2D semantic scene layouts. Our evaluations on synthetic 3D-FRONT and real-world KITTI-360 datasets demonstrate that our model generates scenes of improved visual and geometric quality.
  arXiv  Detail & Related papers  (2023-03-21T17:59:02Z)
• Efficient Geometry-aware 3D Generative Adversarial Networks [50.68436093869381]
  Existing 3D GANs are either compute-intensive or make approximations that are not 3D-consistent. In this work, we improve the computational efficiency and image quality of 3D GANs without overly relying on these approximations. We introduce an expressive hybrid explicit-implicit network architecture that synthesizes not only high-resolution multi-view-consistent images in real time but also produces high-quality 3D geometry.
  arXiv  Detail & Related papers  (2021-12-15T08:01:43Z)

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.