Source-linked AI summary
Multimodal Shape Completion via IMLE
Himanshu Arora, Saurabh Mishra, Shichong Peng, Ke Li, Ali Mahdavi-Amiri
TL;DR
Partial scans may admit multiple plausible completions, while earlier approaches often learned one-to-one mappings or produced structurally similar alternatives. The paper applies conditional IMLE to generate varied complete point clouds from partial inputs. The authors report stronger diversity and competitive or better distance results than prior methods, including completions for inputs with an entire part missing.
Problem
A partial shape may have multiple plausible completions, but early completion methods focused on one-to-one mappings and prior multimodal outputs could remain structurally similar.
Source: §1 · pp. 1–2Method
The method conditions IMLE on a partial point cloud and combines its encoded latent code with noise to generate candidate complete shapes.
Source: §1 · p. 2, §3 · pp. 4–5Results
Takeaways & Limitations
The method produces reasonable, diverse completions for unseen inputs with an entire part removed despite having no part labels during training.
Source: §1 · p. 2, §4 · p. 8The current network cannot generate very detailed features multimodally; the authors attribute this mainly to its simple architecture and suggest refinement networks as a possible remedy.
Source: §4 · p. 9
Abstract
from arXiv · showhide
Shape completion is the problem of completing partial input shapes such as partial scans. This problem finds important applications in computer vision and robotics due to issues such as occlusion or sparsity in real-world data. However, most of the existing research related to shape completion has been focused on completing shapes by learning a one-to-one mapping which limits the diversity and creativity of the produced results. We propose a novel multimodal shape completion technique that is effectively able to learn a one-to-many mapping and generates diverse complete shapes. Our approach is based on the conditional Implicit MaximumLikelihood Estimation (IMLE) technique wherein we condition our inputs on partial 3D point clouds. We extensively evaluate our approach by comparing it to various baselines both quantitatively and qualitatively. We show that our method is superior to alternatives in terms of completeness and diversity of shapes.
1 Introduction
Shape completion must produce complete shapes that preserve the observed geometry, yet a partial scan can support multiple plausible completions. This work uses conditional IMLE to generate structurally diverse completions and reports stronger diversity and competitive or better distance results than prior methods.
1 Introduction
- Shape completion seeks to fill missing portions of partial scans while preserving the geometry already observed.Occlusion can leave substantial portions of scanned objects missing.Source: §1 · p. 1
- The authors target structurally diverse completions for one partial shape, especially when an entire missing part could plausibly take different forms.They contrast this goal with prior one-to-one completion and with outputs that vary mainly in the dimensions of similarly structured parts.Source: §1 · pp. 1–2
- Conditional IMLE maps a partial shape and noise to candidate latent codes, addressing the mode collapse the authors associate with structurally similar GAN completions.The paper reports that IMLE had not previously been successfully used for point-cloud generation and describes its model as having fewer parameters than.Source: §1 · p. 2, Figure 2 · p. 2
- The authors report superior Total Mutual Difference and comparable or superior Unidirectional Haussdorf Distance versus prior methods.They also report generalization to new kinds of incomplete shapes, including inputs with an entire part missing despite no part-level supervision.Source: §1 · p. 2
2 Related Work
The related work reviews generative models and point-cloud networks before situating the paper among shape-completion approaches. It distinguishes multimodal completion of coarse inputs from methods focused on reproducing fine details in dense inputs.
2 Related Work
- The review focuses on deep generative and point-cloud completion methods because missing regions can be geometrically complex; it leaves adjacent tasks to other surveys.It notes that simple surface repair is not fully effective for reproducing missing parts and features.Source: §2 · p. 2
2.1 Generative Models
- VAEs and GANs provide generative-model context, while GAN mode collapse is a concern when multimodal output variety is needed.The review notes IMLE’s use in multimodal image-generation problems as motivation for avoiding mode collapse.Source: §2.1 · p. 3
2.2 Deep Learning on Point Clouds
- PointNet and PointNet++ are presented as foundational point-cloud networks, respectively learning global features and multi-scale features that abstract local patterns.The review notes their use in varied point-cloud applications.Source: §2.2 · p. 3
2.3 Shape Completion
- Shape-completion work spans classical repair and retrieval, deep and multi-stage completion, detailed-feature methods, and GAN-based multimodal approaches.The paper distinguishes its focus on coarse inputs with multiple plausible interpretations from detailed-input methods, and describes a comparison of its unimodal version with VRCNet. It also notes that voxel-based GCA is not directly aligned with its point-cloud, GAN-alternative focus.Source: §2.3 · pp. 3–4
3 Method
The method trains a conditional implicit generative model to produce multiple complete point clouds from one partial input. Its network combines an autoencoder and a noise-conditioned generator, with losses that encourage target coverage and preservation of the partial shape.
3 Method
- Conditional IMLE trains a noise-conditioned generator to produce non-deterministic complete-shape predictions for a given partial point cloud, addressing mode collapse in conditional GANs.IMLE matches each target to a generated sample rather than matching each generated sample to some target, and does not require an adversarial objective.Source: §3 · p. 4
- The network combines an encoder-decoder autoencoder with a generator that maps a partial-shape code and sampled noise to new latent codes, which the decoder turns into multiple complete shapes.The autoencoder is trained on partial and complete shapes using Earth mover’s distance; the network also uses losses on shapes and latent codes.Source: §3 · pp. 5–6, Figure 3 · p. 5
- For each training input, the method samples multiple noise vectors and selects the generated latent vector nearest the encoded complete-shape target, then applies an L1 loss to bring them closer.Requiring generated samples to cover every training shape is described as preventing mode collapse and promoting diversity.Source: §3 · p. 5
- A Unidirectional Hausdorff Distance loss between the partial input and generated complete shape encourages the output to enclose the partial shape.The passage defines its point comparison using points from the partial and generated complete clouds.Source: §3 · p. 6
- At test time, the model needs no accompanying complete shape or nearest-neighbor selection; it decodes outputs generated from the partial input and different random noise vectors.The conditional IMLE objective is conditioned on the given partial shape, with sampled latent codes and a distance metric used in training.Source: §3 · pp. 5–6
4 Experiments and Results
Experiments compare the method with multimodal baselines across several datasets and evaluate diversity, completeness, robustness, and transfer. Results report diversity gains, qualitative shape variety, and completions that respect partial inputs, alongside timing and architectural limits.
Baselines and datasets
- The evaluation uses 3Depn, PartNet, and RobustPointSet, comparing against MSC and kNN-latent, with supplementary comparisons to unimodal methods.3Depn covers Chair, Plane, and Table; PartNet covers Chair, Table, and Lamp; RobustPointSet is used to evaluate transferability.Source: §4 · p. 6
Evaluation
- Across 3Depn and PartNet, the method gains diversity over multimodal baselines and either outperforms them or performs similarly on completeness.Table 1 reports Total Mutual Difference (TMD), where higher values indicate more diversity; Table 2 reports Unidirectional Hausdorff Distance (UHD), where lower values indicate better completion.Source: §4 · p. 6, Table 1 · p. 8, Table 2 · p. 8
- Generated shapes vary across object categories and are shown both against conditional GAN completions and overlapped with their partial inputs.The qualitative results cover categories including Chair, Lamp, Plane, and Bottle.Source: §4 · p. 6, Figure 4 · p. 7, Figure 5 · p. 7
Part-based completion
- When an entire part is removed, the method generates reasonable, diverse completions, including structurally different parts with similar functions despite training without part labels.The variations can be localized to the missing part even though global noise is injected into the architecture.Source: §4 · p. 8, Figure 7 · p. 8
Transfer Learning
- Fine-tuning on RobustPointSet completes missing regions and parts amid dataset artifacts, though samples with little scope for diversity naturally converge to the actual shape.The model is first trained on 3Depn and then fine-tuned on RobustPointSet.Source: §4 · p. 8, Figure 8 · p. 9
Timing Comparison
- Without a discriminator, the method has lower training time than conditional GAN, while both methods require 2 ms inference.The paper reports 13 ms per shape for its training time and 20ms per second for conditional GAN training.Source: §4 · pp. 8–9
Noise Tolerance
- 2.71 x 10−2 TMD and 8.84 x 10−2 UHD are reported for noisy 3Depn Chair inputs.The analysis adds Gaussian noise to the input point clouds; the reported values are Total Mutual Difference and Unidirectional Hausdorff Distance, respectively.Source: §4 · p. 9, Figure 9 · p. 9
Limitations
- The current network cannot generate very detailed features multimodally; the authors attribute this to its simple architecture and suggest refinement networks.Source: §4 · p. 9
5 Conclusion and Future Work
The paper uses IMLE for multimodal shape completion, reporting diverse completions that respect input geometry and are comparable to state-of-the-art unimodal methods. The current method adds missing parts, while style and fine-detail variation remain future work.
5 Conclusion and Future Work
- IMLE is used to produce several plausible completions for a partial shape, with the goal of expanding diversity in shape completion.The authors report that the method avoids mode collapse and respects the partial input's geometry.Source: §5 · p. 9
- The method's completions are comparable with state-of-the-art unimodal techniques and respect geometric features when present.Source: §5 · p. 9
- The current method adds missing parts; adding styles and fine details multimodally is left for future study.Source: §5 · p. 10
2 Comparison with Unimodal Methods
The method's results are comparable with state-of-the-art unimodal shape completion methods. Comparisons cover completeness or Unidirectional Hausdorff Distance, not diversity.
2 Comparison with Unimodal Methods
- Our method performs better on Plane and Table classes, while VRCNet performs better on Chair class.Source: §2 · p. 13, Table 1 · p. 13
- The unimodal comparison is limited to completeness or Unidirectional Hausdorff Distance; the passages provide no basis for comparing diversity.Source: §2 · p. 13
- Qualitative results are similar or superior to state-of-the-art unimodal methods, despite their use of complex geometry-refinement networks.Source: §2 · p. 13
3 Comparison with Multimodal Methods
Against a conditional GAN, the method produces more diverse and clean results. Compared with GCA, it produces diverse outputs with shorter reported inference time.
3 Comparison with Multimodal Methods
- The method produces more diverse and clean results than the conditional GAN in the qualitative comparison.Source: §3 · p. 13
- Both the method and GCA produce diverse results, including varied bases for round and rectangular tops.Source: §3 · p. 13
- Inference takes 0.02 seconds for our method and 0.7 seconds for GCA, which runs its network 100 times per output.Source: §3 · p. 13
4 Real-world Scans
The authors evaluate a model trained on 3Depn using noisy, sparse real-world ScanNet chair scans, reporting successful completion with decent variety.
4 Real-world Scans
- The ScanNet chair scans are noisier and sparser than the other datasets, making them difficult to evaluate on.Source: §4 · p. 13
- A model trained on 3Depn successfully completes real-world ScanNet scans with decent variety.Source: §4 · pp. 13–14