Monocular 3D object parsing is highly desirable in various scenarios including occlusion reasoning and holistic
scene interpretation. We present a deep convolutional neural network (CNN) architecture to localize semantic
parts in 2D image and 3D space while inferring their visibility states, given a single RGB image. Our key insight
is to exploit domain knowledge to regularize the network by deeply supervising its hidden layers, in order to
sequentially infer intermediate concepts associated with the final task. To acquire training data in desired
quantities with ground truth 3D shape and relevant concepts, we render 3D object CAD models to generate
large-scale synthetic data and simulate challenging occlusion configurations between objects. We train the
network only on synthetic data and demonstrate state-of-the-art performances on real image benchmarks
including an extended version of KITTI, PASCAL VOC, PASCAL3D+ and IKEA for 2D and 3D keypoint
localization and instance segmentation. The empirical results substantiate the utility of our deep supervision
scheme by demonstrating effective transfer of knowledge from synthetic data to real images, resulting in less
overfitting compared to standard end-to-end training.
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Instance Segmentation with Mask-supervised Polygonal Regression Transformers
In this paper, we present an end-to-end instance segmentation method that regresses a polygonal boundary for each object instance. This sparse, vectorized boundary representation for objects, while attractive in many downstream computer vision tasks, quickly runs into issues of parity that need to be addressed: parity in supervision and parity in performance when compared to existing pixel-based methods. This is due in part to object instances being annotated with ground-truth in the form of polygonal boundaries
or segmentation masks, yet being evaluated in a convenient manner using only segmentation masks. Our method, BoundaryFormer, is a Transformer based architecture that directly predicts polygons yet uses instance mask segmentations as the ground-truth supervision for computing the loss. We achieve this by developing an end-to-end differentiable model that solely relies on supervision within the mask space through differentiable rasterization. BoundaryFormer matches or surpasses the Mask R-CNN method in terms of instance segmentation quality on both COCO and Cityscapes while exhibiting significantly better transferability across datasets.
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- NSF-PAR ID:
- 10350146
- Date Published:
- Journal Name:
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition
- ISSN:
- 2332-564X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
