Translating a Visual LEGO Manual to a Machine-Executable Plan

• URL: http://arxiv.org/abs/2207.12572v1
• Date: Mon, 25 Jul 2022 23:35:46 GMT
• Title: Translating a Visual LEGO Manual to a Machine-Executable Plan
• Authors: Ruocheng Wang, Yunzhi Zhang, Jiayuan Mao, Chin-Yi Cheng, Jiajun Wu
• Abstract summary: We study the problem of translating an image-based, step-by-step assembly manual created by human designers into machine-interpretable instructions. We present a novel learning-based framework, the Manual-to-Executable-Plan Network (MEPNet), which reconstructs the assembly steps from a sequence of manual images.
• Score: 26.0127179598152
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We study the problem of translating an image-based, step-by-step assembly manual created by human designers into machine-interpretable instructions. We formulate this problem as a sequential prediction task: at each step, our model reads the manual, locates the components to be added to the current shape, and infers their 3D poses. This task poses the challenge of establishing a 2D-3D correspondence between the manual image and the real 3D object, and 3D pose estimation for unseen 3D objects, since a new component to be added in a step can be an object built from previous steps. To address these two challenges, we present a novel learning-based framework, the Manual-to-Executable-Plan Network (MEPNet), which reconstructs the assembly steps from a sequence of manual images. The key idea is to integrate neural 2D keypoint detection modules and 2D-3D projection algorithms for high-precision prediction and strong generalization to unseen components. The MEPNet outperforms existing methods on three newly collected LEGO manual datasets and a Minecraft house dataset.

Related papers

• Unsupervised Learning of Category-Level 3D Pose from Object-Centric Videos [15.532504015622159]
  Category-level 3D pose estimation is a fundamentally important problem in computer vision and robotics. We tackle the problem of learning to estimate the category-level 3D pose only from casually taken object-centric videos.
  arXiv  Detail & Related papers  (2024-07-05T09:43:05Z)
• Neural Assembler: Learning to Generate Fine-Grained Robotic Assembly Instructions from Multi-View Images [24.10809783713574]
  This paper introduces a novel task: translating multi-view images of a structural 3D model into a detailed sequence of assembly instructions. We propose an end-to-end model known as the Neural Assembler.
  arXiv  Detail & Related papers  (2024-04-25T08:53:23Z)
• UPose3D: Uncertainty-Aware 3D Human Pose Estimation with Cross-View and Temporal Cues [55.69339788566899]
  UPose3D is a novel approach for multi-view 3D human pose estimation. It improves robustness and flexibility without requiring direct 3D annotations.
  arXiv  Detail & Related papers  (2024-04-23T00:18:00Z)
• SUGAR: Pre-training 3D Visual Representations for Robotics [85.55534363501131]
  We introduce a novel 3D pre-training framework for robotics named SUGAR. SUGAR captures semantic, geometric and affordance properties of objects through 3D point clouds. We show that SUGAR's 3D representation outperforms state-of-the-art 2D and 3D representations.
  arXiv  Detail & Related papers  (2024-04-01T21:23:03Z)
• Dream2Real: Zero-Shot 3D Object Rearrangement with Vision-Language Models [12.965144877139393]
  We introduce Dream2Real, a robotics framework which integrates vision-language models (VLMs) trained on 2D data into a 3D object rearrangement pipeline. This is achieved by the robot autonomously constructing a 3D representation of the scene, where objects can be rearranged virtually and an image of the resulting arrangement rendered. These renders are evaluated by a VLM, so that the arrangement which best satisfies the user instruction is selected and recreated in the real world with pick-and-place.
  arXiv  Detail & Related papers  (2023-12-07T18:51:19Z)
• 3D-GPT: Procedural 3D Modeling with Large Language Models [47.72968643115063]
  We introduce 3D-GPT, a framework utilizing large language models(LLMs) for instruction-driven 3D modeling. 3D-GPT positions LLMs as proficient problem solvers, dissecting the procedural 3D modeling tasks into accessible segments and appointing the apt agent for each task. Our empirical investigations confirm that 3D-GPT not only interprets and executes instructions, delivering reliable results but also collaborates effectively with human designers.
  arXiv  Detail & Related papers  (2023-10-19T17:41:48Z)
• SHOWMe: Benchmarking Object-agnostic Hand-Object 3D Reconstruction [13.417086460511696]
  We introduce the SHOWMe dataset which consists of 96 videos, annotated with real and detailed hand-object 3D textured meshes. We consider a rigid hand-object scenario, in which the pose of the hand with respect to the object remains constant during the whole video sequence. This assumption allows us to register sub-millimetre-precise groundtruth 3D scans to the image sequences in SHOWMe.
  arXiv  Detail & Related papers  (2023-09-19T16:48:29Z)
• CLIP-Guided Vision-Language Pre-training for Question Answering in 3D Scenes [68.61199623705096]
  We design a novel 3D pre-training Vision-Language method that helps a model learn semantically meaningful and transferable 3D scene point cloud representations. We inject the representational power of the popular CLIP model into our 3D encoder by aligning the encoded 3D scene features with the corresponding 2D image and text embeddings. We evaluate our model's 3D world reasoning capability on the downstream task of 3D Visual Question Answering.
  arXiv  Detail & Related papers  (2023-04-12T16:52:29Z)
• SSR-2D: Semantic 3D Scene Reconstruction from 2D Images [54.46126685716471]
  In this work, we explore a central 3D scene modeling task, namely, semantic scene reconstruction without using any 3D annotations. The key idea of our approach is to design a trainable model that employs both incomplete 3D reconstructions and their corresponding source RGB-D images. Our method achieves the state-of-the-art performance of semantic scene completion on two large-scale benchmark datasets MatterPort3D and ScanNet.
  arXiv  Detail & Related papers  (2023-02-07T17:47:52Z)
• DensePose 3D: Lifting Canonical Surface Maps of Articulated Objects to the Third Dimension [71.71234436165255]
  We contribute DensePose 3D, a method that can learn such reconstructions in a weakly supervised fashion from 2D image annotations only. Because it does not require 3D scans, DensePose 3D can be used for learning a wide range of articulated categories such as different animal species. We show significant improvements compared to state-of-the-art non-rigid structure-from-motion baselines on both synthetic and real data on categories of humans and animals.
  arXiv  Detail & Related papers  (2021-08-31T18:33:55Z)
• Model-based 3D Hand Reconstruction via Self-Supervised Learning [72.0817813032385]
  Reconstructing a 3D hand from a single-view RGB image is challenging due to various hand configurations and depth ambiguity. We propose S2HAND, a self-supervised 3D hand reconstruction network that can jointly estimate pose, shape, texture, and the camera viewpoint. For the first time, we demonstrate the feasibility of training an accurate 3D hand reconstruction network without relying on manual annotations.
  arXiv  Detail & Related papers  (2021-03-22T10:12:43Z)

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.