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@@ -25,7 +25,6 @@ The benchmark provides diverse indoor and outdoor scenes with metric depth annot
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  - Unified indoor and outdoor metric depth estimation
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  - VLM-compatible data format
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  - Dense depth supervision for multimodal foundation models
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- - Supports both geometry prediction and spatial reasoning tasks
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  - Designed for scalable multimodal training
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  ## Paper
@@ -37,7 +36,6 @@ The benchmark provides diverse indoor and outdoor scenes with metric depth annot
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  Please refer to [the official repository](https://github.com/hanxunyu/DepthVLM) for:
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  - Data preprocessing
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- - Training instructions
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  - Evaluation scripts
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  - Visualization examples
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  - Unified indoor and outdoor metric depth estimation
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  - VLM-compatible data format
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  - Dense depth supervision for multimodal foundation models
 
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  - Designed for scalable multimodal training
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  ## Paper
 
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  Please refer to [the official repository](https://github.com/hanxunyu/DepthVLM) for:
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  - Data preprocessing
 
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  - Evaluation scripts
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  - Visualization examples
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