用视觉与点云融合生成高清点云，抗噪抗稀疏。
ObitoNet: Multimodal High-Resolution Point Cloud Reconstruction
- 视觉模型提取语义，点云编码器捕获几何结构
- 跨模态注意力融合信息，重建高分辨率点云
- 适合低质量输入场景，如稀疏或噪声数据
ObitoNet采用交叉注意力机制融合多模态输入：视觉变换器（ViT）从图像中提取语义特征，点云标记器通过最远点采样（FPS）和K近邻（KNN）处理几何信息以捕捉空间结构。融合后的多模态特征输入基于Transformer的解码器，实现高分辨率点云重建。该方法充分利用图像丰富的语义信息与点云精确的几何细节，在稀疏或噪声数据等挑战性条件下仍能生成鲁棒点云。
原文摘要 · Abstract (English)
ObitoNet employs a Cross Attention mechanism to integrate multimodal inputs, where Vision Transformers (ViT) extract semantic features from images and a point cloud tokenizer processes geometric information using Farthest Point Sampling (FPS) and K Nearest Neighbors (KNN) for spatial structure capture. The learned multimodal features are fed into a transformer-based decoder for high-resolution point cloud reconstruction. This approach leverages the complementary strengths of both modalities rich image features and precise geometric details ensuring robust point cloud generation even in challenging conditions such as sparse or noisy data.
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