Source-linked AI summary
PoinTr: Diverse Point Cloud Completion with Geometry-Aware Transformers
Xumin Yu, Yongming Rao, Ziyi Wang, Zuyan Liu, Jiwen Lu, Jie Zhou
TL;DR
Incomplete sensor observations make recovering complete point clouds an important practical problem. PoinTr reformulates completion as set-to-set translation with a geometry-aware transformer encoder-decoder and introduces more diverse benchmarks. It achieves state-of-the-art performance across new and existing benchmarks, while relying on transformer features being invariant to point-proxy order.
Problem
Point clouds are often incomplete because of limited sensor resolution, single viewpoints, and occlusion, motivating recovery of complete shapes.
Method
PoinTr represents local point groups as point proxies and uses a geometry-aware transformer encoder-decoder for point cloud completion.
Results
PoinTr outperforms previous state-of-the-art methods by a large margin on new and existing benchmarks.
Takeaways & Limitations
The paper provides a transformer-based completion architecture together with benchmarks covering more diverse tasks, categories, viewpoints, and incompleteness levels.
Takeaways & Limitations
The approach assumes transformer features remain invariant to the order of point proxies when processing point clouds.
Abstract
from arXiv · showhide
Point clouds captured in real-world applications are often incomplete due to the limited sensor resolution, single viewpoint, and occlusion. Therefore, recovering the complete point clouds from partial ones becomes an indispensable task in many practical applications. In this paper, we present a new method that reformulates point cloud completion as a set-to-set translation problem and design a new model, called PoinTr that adopts a transformer encoder-decoder architecture for point cloud completion. By representing the point cloud as a set of unordered groups of points with position embeddings, we convert the point cloud to a sequence of point proxies and employ the transformers for point cloud generation. To facilitate transformers to better leverage the inductive bias about 3D geometric structures of point clouds, we further devise a geometry-aware block that models the local geometric relationships explicitly. The migration of transformers enables our model to better learn structural knowledge and preserve detailed information for point cloud completion. Furthermore, we propose two more challenging benchmarks with more diverse incomplete point clouds that can better reflect the real-world scenarios to promote future research. Experimental results show that our method outperforms state-of-the-art methods by a large margin on both the new benchmarks and the existing ones. Code is available at https://github.com/yuxumin/PoinTr
1. Introduction
Point cloud completion is motivated by incomplete sensor observations and the need to recover complete shapes. PoinTr reframes completion as set-to-set translation with transformers and introduces broader benchmarks for realistic evaluation.
- PoinTr models completion as set-to-set translation using a transformer encoder-decoder to learn dependencies among point groups.
- The model predicts missing parts while simultaneously upsampling known parts from downsampled partial inputs.
- Incomplete point clouds arise from self-occlusion, light reflection, and limited sensor resolution, making completion increasingly important.
- The proposed benchmarks broaden evaluation from eight to 55 categories, eight viewpoints to all possible viewpoints, and 25% to 75% missing points.
- Across the new and existing benchmarks, PoinTr outperforms previous state-of-the-art methods by a large margin.
2. Related Work
Prior 3D shape-completion methods use structured representations or directly process unordered point clouds, while transformers provide attention mechanisms for long-sequence relationships. PoinTr applies this transformer pipeline to point-cloud completion.
- 3D Shape Completion: Voxel grids and distance fields support 3D convolutions but incur memory and computational burdens, while quantization can lose detailed information.
- 3D Shape Completion: Point clouds use less memory and preserve fine-grained details, but their unordered structure makes conventional convolution operators unsuitable.
- Transformers: Transformers combine self-attention and cross-attention in encoder-decoder architectures for modeling relationships in long sequences.
- PoinTr Pipeline: PoinTr's pipeline downsamples partial inputs, extracts local features, adds position embeddings, predicts point proxies, and completes clouds coarsely to finely.
3. Approach
PoinTr reformulates point cloud completion as set-to-set translation over point proxies and uses a transformer encoder-decoder augmented with explicit geometric modeling. Its pipeline encodes local regions, generates missing proxies and coordinates, recovers detailed points, and supervises unordered outputs with Chamfer Distance.
- Set-to-Set Translation with Transformers: PoinTr models completion as set-to-set translation from partial-cloud point proxies to predicted proxies for missing regions.The transformer encoder produces output features, while the decoder uses dynamic queries to predict missing point proxies.
- Set-to-Set Translation with Transformers: The transformer encoder and decoder use self-attention and cross-attention to combine short- and long-range information and learn structural relationships.Transformer features are invariant to the ordering of point proxies, supporting processing of unordered point clouds.
- Point Proxy: Point proxies encode local point-cloud regions using sampled centers and DGCNN features, with an MLP adding global location information.The proxy feature combines local semantic patterns with a position embedding derived from the proxy center.
- Geometry-aware Transformer Block: The geometry-aware block uses kNN neighborhoods and feature aggregation to model local geometric relations alongside semantic attention features.The geometric and semantic features are concatenated and mapped back to the original dimensions.
- Multi-Scale Point Cloud Generation: A query generator conditions decoder queries on encoder outputs and produces M × 3 coordinates used as centers for detailed missing-part generation.FoldingNet recovers local shapes around these centers, and the predicted missing points are concatenated with the input cloud.
- Optimization: Chamfer Distance is used as the completion loss because it is permutation-invariant and has O(N log N) complexity.The final objective combines supervision for predicted local centers and high-resolution point-cloud outputs.
4. Experiments
The experiments evaluate PoinTr on diverse synthetic benchmarks, established datasets, real-world KITTI scans, and component ablations. PoinTr achieves strong completion performance, including state-of-the-art results and improved detail recovery.
- Benchmark Design: The new benchmarks vary tasks, categories, viewpoints, and incompleteness to better represent diverse point cloud completion scenarios.ShapeNet-55 covers 55 categories, while ShapeNet-34 evaluates generalization from 34 seen to 21 unseen categories.
- ShapeNet-55: ShapeNet-55 results show PoinTr remains effective across diverse categories, including categories with relatively few training samples.PoinTr achieves 0.46 F-Score, compared with 0.24 for GRNet.
- Existing Benchmarks: On the PCN dataset, PoinTr largely improves previous methods and establishes the new state of the art.The model achieves over 0.74 F-Score on PCN, compared with 0.46 F-Score on ShapeNet-55.
- Existing Benchmarks: On KITTI, PoinTr achieves better qualitative and quantitative performance, recovering car boundaries and details such as tires more accurately.The evaluation uses Fidelity and MMD on real-world LiDAR-derived incomplete car point clouds.
- Model Design Analysis: Ablations show that the query generator, DGCNN features, and geometry-aware block each improve the transformer completion model.The query generator improves Chamfer distance by 0.34, while DGCNN features improve performance to 8.69; adding the geometric block further improves results.
- Qualitative Results: Qualitative comparisons indicate that PoinTr preserves higher-fidelity geometric details when input point clouds contain severe or varied incompleteness.For heavily degraded airplane inputs, other methods produce rough shapes while PoinTr recovers more satisfactory geometry details.
5. Conclusion
The paper presents PoinTr as a set-to-set transformer architecture for point cloud completion and introduces more challenging benchmarks. It reports state-of-the-art performance while identifying extension to other 3D tasks as future work.
- Conclusion: PoinTr reformulates point cloud completion as a set-to-set translation task and applies a transformer architecture with technical innovations.The conclusion characterizes the resulting model as achieving state-of-the-art performance.
- Conclusion: Two more challenging benchmarks are proposed to evaluate point cloud completion under more diverse conditions.The conclusion presents benchmark diversification as a central contribution alongside the architecture.
- Future Direction: Extending the transformer architecture to other 3D tasks is identified as an interesting future direction.
A. Implementation Details
The implementation combines a transformer encoder-decoder with DGCNN-based point proxy extraction and geometry-aware local-feature processing. The reported configuration fixes the main optimization, architectural, attention, and neighborhood settings.
- Optimization: PoinTr uses AdamW with an initial learning rate of 0.0005 and weight decay of 0.0005.
- Transformer Configuration: The transformer encoder and decoder use depths of 6 and 8, respectively, with 6 attention heads and hidden dimension 384.
- Local Geometry: DGCNN feature extraction uses k=16, while the geometry-aware block uses k=8 for local neighborhood modeling.
- Point Proxy Extraction: The point proxy extractor hierarchically downsamples the input to 128 center points and applies DGCNN layers to capture local geometric relationships.
B. Technical Details on Transformers
PoinTr uses a transformer encoder-decoder to process point proxies and generate predicted proxies representing missing point-cloud regions. Multi-head attention, feed-forward layers, and explicitly defined projection dimensions form the transformer components.
- Encoder-Decoder Architecture: The encoder processes point proxies through N multi-head self-attention and feed-forward layers, while the decoder combines query embeddings with encoder memory to predict missing point proxies.The architecture is illustrated in Figure 7.
- Multi-head Attention: Multi-head attention jointly attends to information from different representation subspaces at different positions using input values, keys, and queries.
- Multi-head Attention: The attention projections use linear layers for different subspaces, with d_k denoting the input-feature dimension.
- Feed-forward Network (FFN): The feed-forward network contains two linear layers with ReLU activations and dropout.
C. Detailed Experimental Results
The paper reports detailed point-cloud-completion results across object categories and three difficulty settings on ShapeNet-55 and novel ShapeNet-34 categories.
- ShapeNet-55: ShapeNet-55 results compare FoldingNet, PCN, TopNet, PFNet, GRNet, and PoinTr across object categories under simple, moderate, and hard settings.
- ShapeNet-34: ShapeNet-34 results evaluate novel objects from 21 categories across simple, moderate, and hard settings.
D. Complexity Analysis
PoinTr achieves the best reported performance while using relatively low parameter counts and FLOPs, indicating a favorable cost-performance trade-off in the reported comparisons.
- Complexity Analysis: PoinTr achieves the best performance on the newly proposed diverse benchmarks and existing benchmarks.
- Complexity Analysis: PoinTr combines the best performance with relatively low parameters and FLOPs among the compared methods.Average Chamfer distances for ShapeNet-55 and unseen ShapeNet-34 categories are provided as references.
- Complexity Analysis: The complexity analysis compares parameter counts and theoretical computation cost in FLOPs for PoinTr and five existing methods.
E. Visualization of the Predicted Centers
The visualization shows a coarse-to-fine completion process in which predicted local centers capture overall structure before surrounding points are added to form final predictions.
- Coarse-to-Fine Strategy: PoinTr first predicts local centers and then obtains final point-cloud predictions by adding points around those centers.
- Visualization: The predicted point proxies represent the overall point-cloud structure, while the final predictions add finer details.In Figure 8, line (a) shows the partial input and predicted centers, and line (b) shows predicted point clouds.
F. Qualitative Results
Figure 9 presents qualitative ShapeNet-55 results, showing that PoinTr is visually much better than baseline methods.
- Figure 9 provides additional qualitative results on ShapeNet-55.
- PoinTr's results are visually much better than those of baseline methods.
- The comparison in Figure 9 is qualitative rather than numerical.