3D Shape Generation: A Survey

• URL: http://arxiv.org/abs/2506.22678v1
• Date: Fri, 27 Jun 2025 23:06:06 GMT
• Title: 3D Shape Generation: A Survey
• Authors: Nicolas Caytuiro, Ivan Sipiran,
• Abstract summary: Recent advances in deep learning have transformed the field of 3D shape generation.<n>This survey organizes the discussion around three core components: shape representations, generative modeling approaches, and evaluation protocols.<n>We identify open challenges and outline future research directions that could drive progress in controllable, efficient, and high-quality 3D shape generation.
• Score: 0.6445605125467574
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in deep learning have significantly transformed the field of 3D shape generation, enabling the synthesis of complex, diverse, and semantically meaningful 3D objects. This survey provides a comprehensive overview of the current state of the art in 3D shape generation, organizing the discussion around three core components: shape representations, generative modeling approaches, and evaluation protocols. We begin by categorizing 3D representations into explicit, implicit, and hybrid setups, highlighting their structural properties, advantages, and limitations. Next, we review a wide range of generation methods, focusing on feedforward architectures. We further summarize commonly used datasets and evaluation metrics that assess fidelity, diversity, and realism of generated shapes. Finally, we identify open challenges and outline future research directions that could drive progress in controllable, efficient, and high-quality 3D shape generation. This survey aims to serve as a valuable reference for researchers and practitioners seeking a structured and in-depth understanding of this rapidly evolving field.

Related papers

• Advances in Feed-Forward 3D Reconstruction and View Synthesis: A Survey [154.50661618628433]
  3D reconstruction and view synthesis are foundational problems in computer vision, graphics, and immersive technologies such as augmented reality (AR), virtual reality (VR), and digital twins.<n>Recent advances in feed-forward approaches, driven by deep learning, have revolutionized this field by enabling fast and generalizable 3D reconstruction and view synthesis.
  arXiv  Detail & Related papers  (2025-07-19T06:13:25Z)
• Geometry-Aware Preference Learning for 3D Texture Generation [8.953379216683732]
  We propose an end-to-end differentiable preference learning framework that back-propagates human preferences through the entire 3D generative pipeline.<n>We demonstrate the effectiveness of our framework using four proposed novel geometry-aware reward functions.
  arXiv  Detail & Related papers  (2025-06-23T06:24:12Z)
• Recent Advance in 3D Object and Scene Generation: A Survey [14.673302810271219]
  This survey aims to provide readers with a structured understanding of state-of-the-art 3D generation technologies.<n>We focus on three dominant paradigms: layout-guided compositional synthesis, 2D prior-based scene generation, and rule-driven modeling.
  arXiv  Detail & Related papers  (2025-04-16T03:22:06Z)
• 3D Representation Methods: A Survey [0.0]
  3D representation has experienced significant advancements, driven by the increasing demand for high-fidelity 3D models in various applications. This review examines the development and current state of 3D representation methods, highlighting their research trajectories, innovations, strength and weakness.
  arXiv  Detail & Related papers  (2024-10-09T02:01:05Z)
• Enhancing Generalizability of Representation Learning for Data-Efficient 3D Scene Understanding [50.448520056844885]
  We propose a generative Bayesian network to produce diverse synthetic scenes with real-world patterns. A series of experiments robustly display our method's consistent superiority over existing state-of-the-art pre-training approaches.
  arXiv  Detail & Related papers  (2024-06-17T07:43:53Z)
• A Comprehensive Survey of 3D Dense Captioning: Localizing and Describing Objects in 3D Scenes [80.20670062509723]
  3D dense captioning is an emerging vision-language bridging task that aims to generate detailed descriptions for 3D scenes. It presents significant potential and challenges due to its closer representation of the real world compared to 2D visual captioning. Despite the popularity and success of existing methods, there is a lack of comprehensive surveys summarizing the advancements in this field.
  arXiv  Detail & Related papers  (2024-03-12T10:04:08Z)
• Advances in 3D Generation: A Survey [54.95024616672868]
  The field of 3D content generation is developing rapidly, enabling the creation of increasingly high-quality and diverse 3D models. Specifically, we introduce the 3D representations that serve as the backbone for 3D generation. We provide a comprehensive overview of the rapidly growing literature on generation methods, categorized by the type of algorithmic paradigms.
  arXiv  Detail & Related papers  (2024-01-31T13:06:48Z)
• Geometric Deep Learning for Structure-Based Drug Design: A Survey [83.87489798671155]
  Structure-based drug design (SBDD) leverages the three-dimensional geometry of proteins to identify potential drug candidates. Recent advancements in geometric deep learning, which effectively integrate and process 3D geometric data, have significantly propelled the field forward.
  arXiv  Detail & Related papers  (2023-06-20T14:21:58Z)
• 3D objects and scenes classification, recognition, segmentation, and reconstruction using 3D point cloud data: A review [5.85206759397617]
  Three-dimensional (3D) point cloud analysis has become one of the attractive subjects in realistic imaging and machine visions. A significant effort has recently been devoted to developing novel strategies, using different techniques such as deep learning models. Various tasks performed on 3D point could data are investigated, including objects and scenes detection, recognition, segmentation and reconstruction.
  arXiv  Detail & Related papers  (2023-06-09T15:45:23Z)
• Deep Generative Models on 3D Representations: A Survey [81.73385191402419]
  Generative models aim to learn the distribution of observed data by generating new instances. Recently, researchers started to shift focus from 2D to 3D space. representing 3D data poses significantly greater challenges.
  arXiv  Detail & Related papers  (2022-10-27T17:59:50Z)
• Active 3D Shape Reconstruction from Vision and Touch [66.08432412497443]
  Humans build 3D understandings of the world through active object exploration, using jointly their senses of vision and touch. In 3D shape reconstruction, most recent progress has relied on static datasets of limited sensory data such as RGB images, depth maps or haptic readings. We introduce a system composed of: 1) a haptic simulator leveraging high spatial resolution vision-based tactile sensors for active touching of 3D objects; 2) a mesh-based 3D shape reconstruction model that relies on tactile or visuotactile priors to guide the shape exploration; and 3) a set of data-driven solutions with either tactile or visuo
  arXiv  Detail & Related papers  (2021-07-20T15:56:52Z)

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.