Summary of Ssrflow: Semantic-aware Fusion with Spatial Temporal Re-embedding For Real-world Scene Flow, by Zhiyang Lu and Qinghan Chen and Zhimin Yuan and Ming Cheng
SSRFlow: Semantic-aware Fusion with Spatial Temporal Re-embedding for Real-world Scene Flow
by Zhiyang Lu, Qinghan Chen, Zhimin Yuan, Ming Cheng
First submitted to arxiv on: 31 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Scene flow methods for dynamic scene perception face three major challenges: global flow embedding, deformation handling, and generalization to real-world data. A novel approach called Dual Cross Attentive (DCA) integrates semantic contexts for latent fusion and alignment between frames. This is then integrated with Global Fusion Flow Embedding (GF) to initialize flow embedding based on global correlations. To handle deformations in non-rigid objects, the Spatial Temporal Re-embedding (STR) module updates point sequence features at current-level. Novel domain adaptive losses bridge the gap between synthetic and real-world data for motion inference. The proposed approach achieves state-of-the-art performance across various datasets, with outstanding results in real-world LiDAR-scanned situations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Scene flow methods are important for understanding 3D motion in videos. Researchers have been trying to solve three big problems: making sure the method works well globally, handling when objects move and change shape, and making it work well on real-world data. To solve these problems, a new approach called DCA combines information from two frames based on what’s happening in each frame. This helps the method understand how objects are moving and changing over time. The authors also developed a way to make sure their method works well when there are deformations (when objects change shape) and another way to bridge the gap between synthetic (fake) data and real-world data. The results show that this new approach is better than previous methods at understanding 3D motion in videos. |
Keywords
» Artificial intelligence » Alignment » Embedding » Generalization » Inference
