HiQGS: Hierarchical Spatiotemporal Querying for Feed-Forward Gaussian Reconstruction

Key points

• Feed-forward Gaussian reconstruction from sparse posed monocular frames requires reasoning over observations that vary jointly in viewpoint and time.1
• HiQGS introduces a Hierarchical Spatiotemporal Encoder that organizes multi-resolution geometry-appearance visual features according to image location, observation time, and camera pose, thereby preserving complementary appearance and structural cues.1
• Our Time-Adaptive Decoder use target timestamp queries the resulting hierarchical representation to retrieve relevant spatiotemporal evidence and predicts the corresponding Gaussian scene state.1
• Experiments on static and dynamic scene reconstruction benchmarks demonstrate strong rendering quality and geometric accuracy, validating the effectiveness of hierarchical visual encoding and target-time-aware Gaussian querying.1

Sources (1)

• [1]HiQGS: Hierarchical Spatiotemporal Querying for Feed-Forward Gaussian Reconstruction
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 20, 04:10 AM

  We present HiQGS, a query-based hierarchical framework for feed-forward Gaussian scene reconstruction from sparse monocular frames.

  Feed-forward Gaussian reconstruction from sparse posed monocular frames requires reasoning over observations that vary jointly in viewpoint and time.

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