Patch2CAD: Patchwise Embedding Learning for In-the-Wild Shape Retrieval from a Single Image

@article{Kuo2021Patch2CADPE,
  title={Patch2CAD: Patchwise Embedding Learning for In-the-Wild Shape Retrieval from a Single Image},
  author={Weicheng Kuo and Anelia Angelova and Tsung-Yi Lin and Angela Dai},
  journal={2021 IEEE/CVF International Conference on Computer Vision (ICCV)},
  year={2021},
  pages={12569-12579}
}
3D perception of object shapes from RGB image input is fundamental towards semantic scene understanding, grounding image-based perception in our spatially 3dimensional real-world environments. To achieve a mapping between image views of objects and 3D shapes, we leverage CAD model priors from existing large-scale databases, and propose a novel approach towards constructing a joint embedding space between 2D images and 3D CAD models in a patch-wise fashion – establishing correspondences between… 

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References

SHOWING 1-10 OF 58 REFERENCES

Mask2CAD: 3D Shape Prediction by Learning to Segment and Retrieve

Mask2CAD is presented, which jointly detects objects in real-world images and for each detected object, optimizes for the most similar CAD model and its pose, and constructs a joint embedding space between the detected regions of an image corresponding to an object and 3D CAD models, enabling retrieval of CAD models for an input RGB image.

Location Field Descriptors: Single Image 3D Model Retrieval in the Wild

This work presents Location Field Descriptors, a novel approach for single image 3D model retrieval in the wild that significantly outperform the state-of-the-art by up to 20% absolute in multiple 3D retrieval metrics.

Learning Local RGB-to-CAD Correspondences for Object Pose Estimation

This paper solves the key problem of existing methods requiring expensive 3D pose annotations by proposing a new method that matches RGB images to CAD models for object pose estimation and can reliably estimate object pose in RGB images and generalize to object instances not seen during training.

Joint embeddings of shapes and images via CNN image purification

A joint embedding space populated by both 3D shapes and 2D images of objects, where the distances between embedded entities reflect similarity between the underlying objects, which facilitates comparison between entities of either form, and allows for cross-modality retrieval.

Scan2CAD: Learning CAD Model Alignment in RGB-D Scans

This work designs a novel 3D CNN architecture that learns a joint embedding between real and synthetic objects, and from this predicts a correspondence heatmap, which forms a variational energy minimization that aligns a given set of CAD models to the reconstruction.

Scene Recomposition by Learning-Based ICP

  • Hamid IzadiniaS. Seitz
  • Computer Science
    2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • 2020
This work proposes a novel approach for aligning CAD models to 3D scans, based on deep reinforcement learning, which outperforms prior ICP methods in the literature and outperforms both learned local deep feature matching and geometric based alignment methods in real scenes.

ShapeMask: Learning to Segment Novel Objects by Refining Shape Priors

ShapeMask is introduced, which learns the intermediate concept of object shape to address the problem of generalization in instance segmentation to novel categories and significantly outperforms the state-of-the-art when learning across categories.

SceneCAD: Predicting Object Alignments and Layouts in RGB-D Scans

A message-passing graph neural network is proposed to model the inter-relationships between objects and layout, guiding generation of a globally object alignment in a scene by considering the global scene layout.

3D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstruction

The 3D-R2N2 reconstruction framework outperforms the state-of-the-art methods for single view reconstruction, and enables the 3D reconstruction of objects in situations when traditional SFM/SLAM methods fail (because of lack of texture and/or wide baseline).

Pix3D: Dataset and Methods for Single-Image 3D Shape Modeling

A novel model is designed that simultaneously performs 3D reconstruction and pose estimation; this multi-task learning approach achieves state-of-the-art performance on both tasks.
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