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Spatially Invariant Unsupervised 3D Object-Centric Learning and Scene Decomposition
Computer Vision and Pattern Recognition · Submitted 10 Jun 2021
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Experimental results demonstrate that SPAIR3D has strong scalability and is capable of detecting and segmenting an unknown number of objects from a point cloud in an unsupervised manner.
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The abstract, sentence by sentence
We tackle the problem of object-centric learning on point clouds, which is crucial for high-level relational reasoning and scalable machine intelligence. In particular, we introduce a framework, SPAIR3D, to factorize a 3D point cloud into a spatial mixture model where each component corresponds to one object. To model the spatial mixture model on point clouds, we derive the Chamfer Mixture Loss, which fits naturally into our variational training pipeline. Moreover, we adopt an object-specification scheme that describes each object's location relative to its local voxel grid cell. Such a scheme allows SPAIR3D to model scenes with an arbitrary number of objects. We evaluate our method on the task of unsupervised scene decomposition. Experimental results demonstrate that SPAIR3D has strong scalability and is capable of detecting and segmenting an unknown number of objects from a point cloud in an unsupervised manner.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | We tackle the problem of object-centric learning on point clouds, which is crucial for high-level relational reasoning and scalable machine intelligence. | background 1.00 | 0.15 | 0.04 | asserted 1.00 |
| s1 | In particular, we introduce a framework, SPAIR3D, to factorize a 3D point cloud into a spatial mixture model where each component corresponds to one object. | contribution 0.94 | 0.94 | 0.07 | asserted 0.99 |
| s2 | To model the spatial mixture model on point clouds, we derive the Chamfer Mixture Loss, which fits naturally into our variational training pipeline. | method 0.87 | 0.96 | 0.04 | asserted 1.00 |
| s3 | Moreover, we adopt an object-specification scheme that describes each object's location relative to its local voxel grid cell. | method 0.96 | 0.93 | 0.03 | asserted 0.99 |
| s4 | Such a scheme allows SPAIR3D to model scenes with an arbitrary number of objects. | method 0.49 | 0.75 | 0.08 | asserted 0.93 |
| s5 | We evaluate our method on the task of unsupervised scene decomposition. | method 0.75 | 0.92 | 0.03 | asserted 0.82 |
| s6 | Experimental results demonstrate that SPAIR3D has strong scalability and is capable of detecting and segmenting an unknown number of objects from a point cloud in an unsupervised manner. | result 0.95 | 0.96 | 0.84 | asserted 1.00 |
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The paper
Original abstract
We tackle the problem of object-centric learning on point clouds, which is crucial for high-level relational reasoning and scalable machine intelligence. In particular, we introduce a framework, SPAIR3D, to factorize a 3D point cloud into a spatial mixture model where each component corresponds to one object. To model the spatial mixture model on point clouds, we derive the Chamfer Mixture Loss, which fits naturally into our variational training pipeline. Moreover, we adopt an object-specification scheme that describes each object's location relative to its local voxel grid cell. Such a scheme allows SPAIR3D to model scenes with an arbitrary number of objects. We evaluate our method on the task of unsupervised scene decomposition. Experimental results demonstrate that SPAIR3D has strong scalability and is capable of detecting and segmenting an unknown number of objects from a point cloud in an unsupervised manner.
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What this paper does with the arXiv papers it cites
What it cites · 4 sentences without a confident label
Builds on 1 paper
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Inspired by SPAIR [ref] and existing works on 3D point cloud object generation requiring centred-points [ref] , we propose in this paper a VAE-based model called Spatially Invariant Attend, Infer, Repeat in 3D (SPAIR3D), a model that generates spatial mixture distributions on point clouds to discover and segment 3D objects from a point cloud of static scenes.
ShapeNet: An Information-Rich 3D Model Repository · Introduction · 0.92
Uses data or values from 1 paper
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To demonstrate that PGF can reconstruct each object with a dynamic number of points, we train the global VAE on the ShapeNet dataset [ref] , where each object is composed of roughly $2000$ points, and reconstruct the object with a varying number of points.
ShapeNet: An Information-Rich 3D Model Repository · Empirical Evaluation of PGF · 0.99
Cites as background 1 paper
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A major focus of these methods is on joint object representation-learning and segmentation from images and videos via generative models [ref] .
MONet: Unsupervised Scene Decomposition and Representation · Related Work · 0.98
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