A Real World Dataset for Multi-view 3D Reconstruction

• URL: http://arxiv.org/abs/2203.11397v1
• Date: Tue, 22 Mar 2022 00:15:54 GMT
• Title: A Real World Dataset for Multi-view 3D Reconstruction
• Authors: Rakesh Shrestha, Siqi Hu, Minghao Gou, Ziyuan Liu, Ping Tan
• Abstract summary: We present a dataset of 371 3D models of everyday tabletop objects along with their 320,000 real world RGB and depth images. We primarily focus on learned multi-view 3D reconstruction due to the lack of appropriate real world benchmark for the task and demonstrate that our dataset can fill that gap.
• Score: 28.298548207213468
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a dataset of 371 3D models of everyday tabletop objects along with their 320,000 real world RGB and depth images. Accurate annotations of camera poses and object poses for each image are performed in a semi-automated fashion to facilitate the use of the dataset for myriad 3D applications like shape reconstruction, object pose estimation, shape retrieval etc. We primarily focus on learned multi-view 3D reconstruction due to the lack of appropriate real world benchmark for the task and demonstrate that our dataset can fill that gap. The entire annotated dataset along with the source code for the annotation tools and evaluation baselines will be made publicly available.

Related papers

• Zero-Shot Multi-Object Scene Completion [59.325611678171974]
  We present a 3D scene completion method that recovers the complete geometry of multiple unseen objects in complex scenes from a single RGB-D image. Our method outperforms the current state-of-the-art on both synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2024-03-21T17:59:59Z)
• Multi-Modal Dataset Acquisition for Photometrically Challenging Object [56.30027922063559]
  This paper addresses the limitations of current datasets for 3D vision tasks in terms of accuracy, size, realism, and suitable imaging modalities for photometrically challenging objects. We propose a novel annotation and acquisition pipeline that enhances existing 3D perception and 6D object pose datasets.
  arXiv  Detail & Related papers  (2023-08-21T10:38:32Z)
• AutoDecoding Latent 3D Diffusion Models [95.7279510847827]
  We present a novel approach to the generation of static and articulated 3D assets that has a 3D autodecoder at its core. The 3D autodecoder framework embeds properties learned from the target dataset in the latent space. We then identify the appropriate intermediate volumetric latent space, and introduce robust normalization and de-normalization operations.
  arXiv  Detail & Related papers  (2023-07-07T17:59:14Z)
• 3D Reconstruction of Objects in Hands without Real World 3D Supervision [12.70221786947807]
  We propose modules to leverage 3D supervision to scale up the learning of models for reconstructing hand-held objects. Specifically, we extract multiview 2D mask supervision from videos and 3D shape priors from shape collections. We use these indirect 3D cues to train occupancy networks that predict the 3D shape of objects from a single RGB image.
  arXiv  Detail & Related papers  (2023-05-04T17:56:48Z)
• Anything-3D: Towards Single-view Anything Reconstruction in the Wild [61.090129285205805]
  We introduce Anything-3D, a methodical framework that ingeniously combines a series of visual-language models and the Segment-Anything object segmentation model. Our approach employs a BLIP model to generate textural descriptions, utilize the Segment-Anything model for the effective extraction of objects of interest, and leverages a text-to-image diffusion model to lift object into a neural radiance field.
  arXiv  Detail & Related papers  (2023-04-19T16:39:51Z)
• MobileBrick: Building LEGO for 3D Reconstruction on Mobile Devices [78.20154723650333]
  High-quality 3D ground-truth shapes are critical for 3D object reconstruction evaluation. We introduce a novel multi-view RGBD dataset captured using a mobile device. We obtain precise 3D ground-truth shape without relying on high-end 3D scanners.
  arXiv  Detail & Related papers  (2023-03-03T14:02:50Z)
• HM3D-ABO: A Photo-realistic Dataset for Object-centric Multi-view 3D Reconstruction [37.29140654256627]
  We present a photo-realistic object-centric dataset HM3D-ABO. It is constructed by composing realistic indoor scene and realistic object. The dataset could also be useful for tasks such as camera pose estimation and novel-view synthesis.
  arXiv  Detail & Related papers  (2022-06-24T16:02:01Z)
• ABO: Dataset and Benchmarks for Real-World 3D Object Understanding [43.42504014918771]
  Amazon-Berkeley Objects (ABO) is a large-scale dataset of product images and 3D models corresponding to real household objects. We use ABO to measure the domain gap for single-view 3D reconstruction networks trained on synthetic objects. We also use multi-view images from ABO to measure the robustness of state-of-the-art metric learning approaches to different camera viewpoints.
  arXiv  Detail & Related papers  (2021-10-12T17:52:42Z)
• D3D-HOI: Dynamic 3D Human-Object Interactions from Videos [49.38319295373466]
  We introduce D3D-HOI: a dataset of monocular videos with ground truth annotations of 3D object pose, shape and part motion during human-object interactions. Our dataset consists of several common articulated objects captured from diverse real-world scenes and camera viewpoints. We leverage the estimated 3D human pose for more accurate inference of the object spatial layout and dynamics.
  arXiv  Detail & Related papers  (2021-08-19T00:49:01Z)
• Objectron: A Large Scale Dataset of Object-Centric Videos in the Wild with Pose Annotations [0.0]
  We introduce the Objectron dataset to advance the state of the art in 3D object detection. The dataset contains object-centric short videos with pose annotations for nine categories and includes 4 million annotated images in 14,819 annotated videos.
  arXiv  Detail & Related papers  (2020-12-18T00:34:18Z)

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.