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Self-Supervised Deep Learning on Point Clouds by Reconstructing Space

Jonathan Sauder, Bjarne Sievers

arXiv:1901.08396v2cs.LGcs.CVstat.ML

TL;DR

Massive point-cloud datasets are difficult to annotate, motivating learning from unlabelled data. The paper trains networks to reconstruct point clouds with randomly rearranged parts, then reports gains in downstream classification, supervised performance, and sample efficiency.

  • Problem

    Massive point-cloud datasets can be difficult to annotate manually, motivating methods that reduce the number of labeled samples needed for strong supervised performance.

    Source: §1 · p. 1

  • Method

    The method trains a neural network to reconstruct point clouds whose parts have been randomly displaced, using an architecture-agnostic self-supervised task.

    Source: §1 · p. 2

  • Results

    The method outperforms state-of-the-art unsupervised methods in downstream object classification, and pretraining improves results across evaluated supervised tasks.

    Source: §1 · p. 2

  • Takeaways & Limitations

    Pretraining with the method can improve sample efficiency and the accuracy of state-of-the-art models before fully supervised training.

    Source: §6 · p. 8

Abstract

from arXiv · show

Point clouds provide a flexible and natural representation usable in countless applications such as robotics or self-driving cars. Recently, deep neural networks operating on raw point cloud data have shown promising results on supervised learning tasks such as object classification and semantic segmentation. While massive point cloud datasets can be captured using modern scanning technology, manually labelling such large 3D point clouds for supervised learning tasks is a cumbersome process. This necessitates methods that can learn from unlabelled data to significantly reduce the number of annotated samples needed in supervised learning. We propose a self-supervised learning task for deep learning on raw point cloud data in which a neural network is trained to reconstruct point clouds whose parts have been randomly rearranged. While solving this task, representations that capture semantic properties of the point cloud are learned. Our method is agnostic of network architecture and outperforms current unsupervised learning approaches in downstream object classification tasks. We show experimentally, that pre-training with our method before supervised training improves the performance of state-of-the-art models and significantly improves sample efficiency.

1 Introduction

Point-cloud datasets can be enormous, making manual annotation difficult, while conventional geometric and hand-crafted features lack flexibility across tasks. The paper introduces architecture-agnostic self-supervised pretraining and reports stronger downstream classification and improvements across evaluated supervised tasks.

1 Introduction

  • Traditional geometric and hand-crafted features are often tailored to specific tasks, whereas CNNs have shown promising supervised performance on point-cloud classification and segmentation.The passage identifies faces, edges, corners, and statistical properties as examples of conventional features.Source: §1 · p. 1
  • Massive point-cloud datasets can contain billions of points and petabytes of data, making manual annotation difficult and motivating methods that reduce annotated-sample needs.The passage cites limitations of labeling interfaces for 3D data on 2D screens.Source: §1 · p. 1
  • The proposed architecture-agnostic task trains a network to reconstruct point clouds whose parts have been randomly displaced.The method is intended to pretrain networks for other raw-point-cloud tasks.Source: §1 · p. 2
  • The method outperforms state-of-the-art unsupervised methods in downstream object classification and improves results in all evaluated supervised tasks.The authors also report improved sample efficiency when pretraining precedes supervised learning.Source: §1 · p. 2

2 Related Work

Deep-learning approaches to point clouds range from structured representations to networks that operate directly on raw points. Unsupervised and self-supervised methods offer an alternative when annotations are scarce, but existing point-cloud approaches have practical and metric-related constraints.

2.1 Deep Learning on Point Clouds

  • Voxel representations face resolution and 3D-convolution cost constraints and can be difficult with varying point density; multi-view methods have uncertain extension to larger scenes and per-point tasks.Multi-view methods have shown strong standalone-object classification performance.Source: §2.1 · p. 3
  • Raw-point networks must handle unordered sets; PointNet uses max pooling for permutation invariance, while later methods add local context, dynamic graph convolutions, or learned hierarchical convolutions.Raw-point networks also provide per-point embeddings useful for segmentation.Source: §2.1 · p. 3

2.2 Unsupervised and Self-Supervised Deep Learning

  • Unsupervised or self-supervised representations can serve downstream tasks or initialize supervised models, potentially reducing the annotated samples needed when data are plentiful but labels are scarce.The passage frames this as a motivation for point-cloud learning.Source: §2.2 · p. 3
  • Point-cloud GAN approaches are constrained by the difficulty of sampling unordered, interdependent point sets, while raw-point autoencoders require similarity metrics such as Chamfer distance.Chamfer computation can be memory-limited on large point clouds, and specific pathological cases may be handled incorrectly.Source: §2.2 · p. 3
  • Image self-supervision has used patch-relation, patch-reordering, and rotation-prediction tasks, but identified risks of trivial solutions or dependence on a defined height axis.The paper adapts patch reordering to point clouds [21].Source: §2.2 · p. 4

3 Method

The method casts self-supervised learning on raw point clouds as point segmentation: points receive voxel-derived labels, voxels are shuffled, and a network predicts the original labels. This avoids point-cloud similarity losses and can use point-segmentation architectures directly.

3 Method

  • The task trains a network to reassemble point-cloud segments displaced from their original positions, under the assumption that doing so requires holistic, high-level object representations.This is the method's stated key assumption, rather than a separately established guarantee.Source: §3 · p. 4
  • The input is scaled to a unit cube, divided along each axis into k equal lengths, and each point receives its voxel ID as a label before the voxels are randomly swapped.Points may also be augmented, for example by small random shifts; results use k = 3.Source: §3 · p. 4
  • The method is architecture-agnostic and can pretrain point-segmentation networks such as PointNet, PointNet++, DGCNN, or PointCNN for supervised use.The task does not require limiting the network's receptive field, according to the authors' contrast with image-domain trivial solutions [7] [21].Source: §3 · p. 4
  • Framing the task as point segmentation avoids potentially flawed similarity metrics and sampling unordered point sets, and operates on raw point clouds rather than voxelized or rendered inputs.The authors also describe raw-point operation as flexible to point-cloud density and suitable for per-point embeddings.Source: §3 · pp. 4–5

4 Experiments

The experiments evaluate self-supervised embeddings and pre-training across object classification, part segmentation, and semantic segmentation. They report stronger classification than prior unsupervised methods and improvements from pre-training across the evaluated segmentation and classification settings.

4.1 Object Classification

  • The learned embeddings outperform previous unsupervised methods on ModelNet40 classification and all but Latent-GAN on ModelNet10, whose comparison is inconclusive because its sampling procedure is unavailable.The transfer experiment pre-trains on ShapeNet and evaluates a linear SVM on ModelNet embeddings.Source: §4.1 · pp. 5–6
  • Self-supervised DGCNN pre-training on ShapeNet reduces supervised epochs needed for strong ModelNet40 classification and improves final accuracy.Pre-training used 1024 points per model for 100 epochs.Source: §4.1 · p. 6, Table 2 · p. 6

4.2 Part Segmentation

  • A pre-trained DGCNN slightly outperforms a randomly initialized model on ShapeNet Part segmentation, with particularly notable differences for classes with few samples.The evaluation uses mean IoU of parts per object class.Source: §4.2 · p. 7, Table 3 · p. 7

4.3 Semantic Segmentation

  • Pre-training improves S3DIS semantic-segmentation mIoU and classification accuracy in all but two tied cases, with the largest difference in the training setting with the fewest samples.The features also show object-part structure without part-ID training; visualizations show separable airplane and chair feature clusters and clusters for individual parts.Source: §4.2 · p. 7, Figure 4 · p. 7, §4.3 · p. 8

5 Discussion

The discussion reports that the reconstruction task appears to learn useful representations without a significant trivial-solution problem, and identifies the chosen voxel setup and its scope.

5 Discussion

  • The authors consider trivial solutions to reconstruction not a significant problem for point clouds, which can be freely shifted and rotated without chromatic aberration.They contrast this with concerns discussed for the image domain.Source: §5 · p. 8
  • A three-by-three voxel grid outperformed k = 2 and k = 4 in the authors’ experiments and was used throughout.Source: §5 · p. 8
  • Randomly rotating 15% of voxels and replacing one voxel per input with a random voxel from another same-dataset input yielded around 0.2% higher downstream SVM accuracy.The authors retained this setup throughout their experiments.Source: §5 · p. 8
  • Extensive dataset- or domain-specific fine-tuning was outside the paper’s scope; the authors report that the simple approach worked reliably in all evaluated cases.Source: §5 · p. 8

6 Conclusion

The conclusion presents self-supervised reconstruction of displaced point-cloud parts as a way to learn representations from unlabelled data. It reports gains in downstream tasks and sample efficiency, and notes the method’s architecture independence.

6 Conclusion

  • The method trains a neural network to reconstruct unlabelled raw point clouds whose parts have been randomly displaced, learning high-level representations.Source: §6 · p. 8
  • Pre-training with the method improves state-of-the-art models’ accuracy and sample efficiency in downstream tasks.The paper reports using large amounts of data before fully supervised training.Source: §6 · p. 8
  • Because the method is independent of the network architecture, the authors expect further benefits as more effective point-cloud networks are developed.Source: §6 · p. 8

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