AdaPoinTr: Diverse Point Cloud Completion with Adaptive Geometry-Aware Transformers (2301.04545v1)

Abstract: 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, which 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 input data to a sequence of point proxies and employ the Transformers for 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. Taking a step towards more complicated and diverse situations, we further propose AdaPoinTr by developing an adaptive query generation mechanism and designing a novel denoising task during completing a point cloud. Coupling these two techniques enables us to train the model efficiently and effectively: we reduce training time (by 15x or more) and improve completion performance (over 20%). We also show our method can be extended to the scene-level point cloud completion scenario by designing a new geometry-enhanced semantic scene completion framework. Extensive experiments on the existing and newly-proposed datasets demonstrate the effectiveness of our method, which attains 6.53 CD on PCN, 0.81 CD on ShapeNet-55 and 0.392 MMD on real-world KITTI, surpassing other work by a large margin and establishing new state-of-the-arts on various benchmarks. Most notably, AdaPoinTr can achieve such promising performance with higher throughputs and fewer FLOPs compared with the previous best methods in practice. The code and datasets are available at https://github.com/yuxumin/PoinTr

• The paper introduces AdaPoinTr, a novel Transformer-based approach that reformulates point cloud completion as a set-to-set translation task using geometry-aware blocks.
• It leverages adaptive query generation and an auxiliary denoising task to efficiently model local 3D structures and stabilize training.
• AdaPoinTr achieves state-of-the-art performance with over 20% accuracy improvement and 15x faster training on benchmarks like ShapeNet-55 and KITTI.

Overview of "AdaPoinTr: Diverse Point Cloud Completion with Adaptive Geometry-Aware Transformers"

The paper "AdaPoinTr: Diverse Point Cloud Completion with Adaptive Geometry-Aware Transformers" introduces a novel approach to the problem of point cloud completion—a critical task in 3D computer vision where incomplete point clouds captured by sensors are converted into complete 3D models. The proposed method, leveraging a Transformer-based architecture, represents a shift from conventional high-resolution 3D methods requiring vast computational resources, focusing instead on efficiency and accuracy through adaptive geometric modeling.

Methodology

Core Architecture:

The foundation of the approach is the Transformer model, traditionally successful in NLP, adapted to 3D vision tasks. The method reformulates point cloud completion as a set-to-set translation problem by representing point clouds as sequences of unordered groups.

• Point Proxy Representation: Point clouds are initially converted into 'point proxies' using a lightweight DGCNN, permitting the use of point-wise features while maintaining their unordered nature, essential for Transformers.
• Geometry-Aware Transformer Block: A crucial innovation is the geometry-aware block that introduces inductive biases regarding the 3D geometric structure into the self-attention mechanism. This block efficiently models local geometric relationships within the point cloud.

Adaptive Innovations:

Two major adaptations enhance the Transformer’s performance in this context:

• Adaptive Query Generation: Queries in the Transformer decoder are dynamically generated from the encoder's outputs, improving flexibility and the model’s ability to adapt to varying object complexities and partialities in point clouds.
• Auxiliary Denoising Task: This component addresses the inherent challenges in training Transformer models with low-quality initial queries by introducing noised queries, thereby stabilizing and accelerating the training process.

Results

The authors present extensive numerical results demonstrating the superiority of AdaPoinTr over existing methods. Notable improvements are achieved in both computational efficiency (15x reduction in training time) and accuracy (over 20% performance improvement in benchmark tests).

• Benchmark Performance: AdaPoinTr establishes new state-of-the-art results across multiple datasets, including ShapeNet-55 (0.81 CD) and KITTI (0.392 MMD), surpassing existing models significantly.
• Robustness and Generalization: The method shows robust performance in both object-level and scene-level completion tasks, generalizing effectively to diverse and previously unseen categories.

Implications and Future Perspectives

The implications of AadPoinTr's approach are twofold:

1. Practical Impact: The reduction in computational overhead while increasing completion fidelity makes it feasible for real-time applications in industry where resource constraints are a concern.
2. Theoretical Impact: By successfully applying sophisticated Transformer architectures to 3D vision, this work opens avenues for further research in leveraging Transformers’ self-attention capabilities to capture intricate spatial relationships in high-dimensional tasks.

As such, researchers may explore adapting Transformer-based architectures to other dimensions of 3D data processing and analysis, such as real-time 3D reconstruction and enhanced depth sensing.

Ultimately, the contribution of AdaPoinTr lies in its ability to robustly handle the diversity and complexity inherent in real-world 3D data, setting a new benchmark for future research in adaptive geometry-aware modeling.