CAPNet: Continuous Approximation Projection for 3D Point Cloud Reconstruction Using 2D Supervision

Author:

Navaneet K. L.,Mandikal Priyanka,Agarwal Mayank,Babu R. Venkatesh

Abstract

Knowledge of 3D properties of objects is a necessity in order to build effective computer vision systems. However, lack of large scale 3D datasets can be a major constraint for datadriven approaches in learning such properties. We consider the task of single image 3D point cloud reconstruction, and aim to utilize multiple foreground masks as our supervisory data to alleviate the need for large scale 3D datasets. A novel differentiable projection module, called ‘CAPNet’, is introduced to obtain such 2D masks from a predicted 3D point cloud. The key idea is to model the projections as a continuous approximation of the points in the point cloud. To overcome the challenges of sparse projection maps, we propose a loss formulation termed ‘affinity loss’ to generate outlierfree reconstructions. We significantly outperform the existing projection based approaches on a large-scale synthetic dataset. We show the utility and generalizability of such a 2D supervised approach through experiments on a real-world dataset, where lack of 3D data can be a serious concern. To further enhance the reconstructions, we also propose a test stage optimization procedure to obtain reconstructions that display high correspondence with the observed input image.

Publisher

Association for the Advancement of Artificial Intelligence (AAAI)

Subject

General Medicine

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1. Self-supervised single-view 3D point cloud reconstruction through GAN inversion;The Journal of Supercomputing;2024-06-08

2. Local Topology Constrained Point Cloud Registration in Building Information Modeling;IEEE Sensors Journal;2024-02-01

3. Leveraging Transformer and CNN for Monocular 3D Point Cloud Reconstruction;2023 IEEE International Conference on Wireless for Space and Extreme Environments (WiSEE);2023-09-06

4. Surface Reconstruction Based on Point Cloud Separation and Boundary RMLS;Proceedings of the 2023 7th International Conference on Graphics and Signal Processing;2023-06-23

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