无需图像和对应关系，用点云序列重建完整动态三维模型
DynaTok: Token-Based 4D Reconstruction from Partial Point Clouds
- 将不完整点云帧编码为紧凑隐向量，用Transformer融合时空信息
- 在物体级和场景级基准上实现更优的重建质量与时间一致性
- 适合缺乏图像、对应关系或输入不完整的动态3D重建任务
针对深度传感器观测不完整、无序且缺乏显式时序对应关系的4D重建问题，本文提出DynaTok——一种无需图像、无需显式对应关系的基于点云的4D重建框架。该方法将各帧编码为紧凑的潜在令牌，通过基于Transformer的时空编码器聚合不完整观测，并在统一模型中通过残差令牌解耦几何与运动。流匹配解码器基于隐向量重建完整且时序一致的4D点云序列。在物体级与场景级基准上的实验表明，该方法能从部分点云序列中获得更高重建质量与更强时间连贯性。
原文摘要 · Abstract (English)
We address 4D reconstruction from partial point cloud sequences, where depth-sensor observations are incomplete, unordered, and lack explicit temporal correspondences. This geometry-only setting is challenging due to missing observations and ambiguous dynamics. While recent progress has largely relied on image-based methods, existing point-based approaches typically focus on single objects, assume relatively complete inputs, or require explicit correspondences. To address these limitations, we propose DynaTok, a point-based framework for correspondence-free 4D reconstruction from partial point cloud sequences without images. DynaTok encodes frames into compact latent tokens, aggregates incomplete observations over time with a Transformer-based spatiotemporal encoder, and decouples geometry and motion through residual tokens in a unified model. A flow-matching decoder then reconstructs complete, temporally consistent 4D point-cloud sequences conditioned on the latent tokens. Experiments on object- and scene-level benchmarks demonstrate improved reconstruction quality and temporal coherence from partial point cloud observations. Project page: https://wrchen530.github.io/dynatok/.
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