# Multiview Aggregation for Learning Category-Specific Shape Reconstruction

arXiv: https://arxiv.org/abs/1907.01085 · HTML: https://mlsanity.com/paper/1907.01085/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jul 2019.

## Built on by (most cited first, 12 of 12)

- [ConDor: Self-Supervised Canonicalization of 3D Pose for Partial Shapes](https://mlsanity.com/paper/2201.07788/) (2022)
- [Unsupervised Learning of Fine Structure Generation for 3D Point Clouds by 2D Projection Matching](https://mlsanity.com/paper/2108.03746/) (2021)
- [StrobeNet: Category-Level Multiview Reconstruction of Articulated Objects](https://mlsanity.com/paper/2105.08016/) (2021)
- [Canonical Fields: Self-Supervised Learning of Pose-Canonicalized Neural Fields](https://mlsanity.com/paper/2212.02493/) (2022)
- [SSDNeRF: Semantic Soft Decomposition of Neural Radiance Fields](https://mlsanity.com/paper/2212.03406/) (2022)
- [Jigsaw++: Imagining Complete Shape Priors for Object Reassembly](https://mlsanity.com/paper/2410.11816/) (2024)
- [Weak Multi-View Supervision for Surface Mapping Estimation](https://mlsanity.com/paper/2105.01388/) (2021)
- [SE-MD: A Single-encoder multiple-decoder deep network for point cloud generation from 2D images](https://mlsanity.com/paper/2106.15325/) (2021)
- [Set Twister for Single-hop Node Classification](https://mlsanity.com/paper/2112.09752/) (2021)
- [Glissando-Net: Deep sinGLe vIew category level poSe eStimation ANd 3D recOnstruction](https://mlsanity.com/paper/2501.14896/) (2025)
- [DiffS-NOCS: 3D Point Cloud Reconstruction through Coloring Sketches to NOCS Maps Using Diffusion Models](https://mlsanity.com/paper/2506.12835/) (2025)
- [3DMPE: 3D Multi-Perspective Embedding](https://mlsanity.com/paper/2607.04898/) (2026)

## Abstract (arXiv)

We investigate the problem of learning category-specific 3D shape reconstruction from a variable number of RGB views of previously unobserved object instances. Most approaches for multiview shape reconstruction operate on sparse shape representations, or assume a fixed number of views. We present a method that can estimate dense 3D shape, and aggregate shape across multiple and varying number of input views. Given a single input view of an object instance, we propose a representation that encodes the dense shape of the visible object surface as well as the surface behind line of sight occluded by the visible surface. When multiple input views are available, the shape representation is designed to be aggregated into a single 3D shape using an inexpensive union operation. We train a 2D CNN to learn to predict this representation from a variable number of views (1 or more). We further aggregate multiview information by using permutation equivariant layers that promote order-agnostic view information exchange at the feature level. Experiments show that our approach is able to produce dense 3D reconstructions of objects that improve in quality as more views are added.