Towards 3D VR-Sketch to 3D Shape Retrieval

• URL: http://arxiv.org/abs/2209.10020v2
• Date: Sun, 18 Feb 2024 11:59:16 GMT
• Title: Towards 3D VR-Sketch to 3D Shape Retrieval
• Authors: Ling Luo, Yulia Gryaditskaya, Yongxin Yang, Tao Xiang, Yi-Zhe Song
• Abstract summary: We study the use of 3D sketches as an input modality and advocate a VR-scenario where retrieval is conducted. As a first stab at this new 3D VR-sketch to 3D shape retrieval problem, we make four contributions.
• Score: 128.47604316459905
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Growing free online 3D shapes collections dictated research on 3D retrieval. Active debate has however been had on (i) what the best input modality is to trigger retrieval, and (ii) the ultimate usage scenario for such retrieval. In this paper, we offer a different perspective towards answering these questions -- we study the use of 3D sketches as an input modality and advocate a VR-scenario where retrieval is conducted. Thus, the ultimate vision is that users can freely retrieve a 3D model by air-doodling in a VR environment. As a first stab at this new 3D VR-sketch to 3D shape retrieval problem, we make four contributions. First, we code a VR utility to collect 3D VR-sketches and conduct retrieval. Second, we collect the first set of $167$ 3D VR-sketches on two shape categories from ModelNet. Third, we propose a novel approach to generate a synthetic dataset of human-like 3D sketches of different abstract levels to train deep networks. At last, we compare the common multi-view and volumetric approaches: We show that, in contrast to 3D shape to 3D shape retrieval, volumetric point-based approaches exhibit superior performance on 3D sketch to 3D shape retrieval due to the sparse and abstract nature of 3D VR-sketches. We believe these contributions will collectively serve as enablers for future attempts at this problem. The VR interface, code and datasets are available at https://tinyurl.com/3DSketch3DV.

Related papers

• OVIR-3D: Open-Vocabulary 3D Instance Retrieval Without Training on 3D Data [15.53270401654078]
  OVIR-3D is a method for open-vocabulary 3D object instance retrieval without using any 3D data for training. It is achieved by a multi-view fusion of text-aligned 2D region proposals into 3D space. Experiments on public datasets and a real robot show the effectiveness of the method and its potential for applications in robot navigation and manipulation.
  arXiv  Detail & Related papers  (2023-11-06T05:00:00Z)
• PonderV2: Pave the Way for 3D Foundation Model with A Universal Pre-training Paradigm [114.47216525866435]
  We introduce a novel universal 3D pre-training framework designed to facilitate the acquisition of efficient 3D representation. For the first time, PonderV2 achieves state-of-the-art performance on 11 indoor and outdoor benchmarks, implying its effectiveness.
  arXiv  Detail & Related papers  (2023-10-12T17:59:57Z)
• 3D-LLM: Injecting the 3D World into Large Language Models [60.43823088804661]
  Large language models (LLMs) and Vision-Language Models (VLMs) have been proven to excel at multiple tasks, such as commonsense reasoning. We propose to inject the 3D world into large language models and introduce a new family of 3D-LLMs. Specifically, 3D-LLMs can take 3D point clouds and their features as input and perform a diverse set of 3D-related tasks.
  arXiv  Detail & Related papers  (2023-07-24T17:59:02Z)
• Learning 3D Scene Priors with 2D Supervision [37.79852635415233]
  We propose a new method to learn 3D scene priors of layout and shape without requiring any 3D ground truth. Our method represents a 3D scene as a latent vector, from which we can progressively decode to a sequence of objects characterized by their class categories. Experiments on 3D-FRONT and ScanNet show that our method outperforms state of the art in single-view reconstruction.
  arXiv  Detail & Related papers  (2022-11-25T15:03:32Z)
• Fine-Grained VR Sketching: Dataset and Insights [140.0579567561475]
  We present the first fine-grained dataset of 1,497 3D VR sketch and 3D shape pairs of a chair category with large shapes diversity. Our dataset supports the recent trend in the sketch community on fine-grained data analysis.
  arXiv  Detail & Related papers  (2022-09-20T21:30:54Z)
• Structure-Aware 3D VR Sketch to 3D Shape Retrieval [113.20120789493217]
  We focus on the challenge caused by inherent inaccuracies in 3D VR sketches. We propose to use a triplet loss with an adaptive margin value driven by a "fitting gap" We introduce a dataset of 202 VR sketches for 202 3D shapes drawn from memory rather than from observation.
  arXiv  Detail & Related papers  (2022-09-19T14:29:26Z)
• Gait Recognition in the Wild with Dense 3D Representations and A Benchmark [86.68648536257588]
  Existing studies for gait recognition are dominated by 2D representations like the silhouette or skeleton of the human body in constrained scenes. This paper aims to explore dense 3D representations for gait recognition in the wild. We build the first large-scale 3D representation-based gait recognition dataset, named Gait3D.
  arXiv  Detail & Related papers  (2022-04-06T03:54:06Z)

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.