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Hunan Normal University

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Research library67linked papers
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Selected work

Representative Papers

Toward AI-Friendly Cartography: Understanding How Color Design Influences Foundation Model Spatial Reasoning on Sequential Choropleth Maps

Aug 16, 2026

This study addresses the unknown applicability of traditional cartographic color principles to spatial reasoning in foundation models. By constructing a controlled benchmark and integrating multimodal evaluation, LoRA fine-tuning, and factorial experiments, this work systematically quantifies the impact of color variables on model reasoning for the first time. Results indicate that disordered color sequences and low contrast significantly impair performance, and notably, fine-tuning fails to eliminate this sensitivity. Highlighting the critical roles of sequential color ordering and contrast, this research proposes AI-friendly cartographic design guidelines. These findings provide empirical evidence and methodological guidance for optimizing map understanding capabilities in artificial intelligence systems, bridging the gap between classical cartography and modern vision-language models.

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FPSGen: Flexible Point Cloud Scene Generation with BEV-Supported Transport Flows

Jul 29, 2026

Existing point cloud scene generation methods rely on partial scans as conditioning inputs, leading to a mismatch between training and inference, poor handling of sparsity in distant regions and occluded areas, and limited flexibility in generating scenes without LiDAR observations. To address these limitations, this work proposes a unified generation framework that dispenses with partial scans by predicting density, height, and occupancy masks in bird’s-eye view (BEV) to construct structured point sources. Furthermore, it introduces a teacher–student approximate optimal transport mechanism that learns straighter transport paths for efficient single-step point generation. The approach supports both unconditional and multi-cue conditional generation, achieving state-of-the-art Jensen–Shannon divergence (JSD) and voxel IoU on SemanticKITTI, and the best Coverage score on KITTI-360 under unconditional generation.

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Recent publications

Latest Papers

Toward AI-Friendly Cartography: Understanding How Color Design Influences Foundation Model Spatial Reasoning on Sequential Choropleth Maps

Aug 16, 2026

This study addresses the unknown applicability of traditional cartographic color principles to spatial reasoning in foundation models. By constructing a controlled benchmark and integrating multimodal evaluation, LoRA fine-tuning, and factorial experiments, this work systematically quantifies the impact of color variables on model reasoning for the first time. Results indicate that disordered color sequences and low contrast significantly impair performance, and notably, fine-tuning fails to eliminate this sensitivity. Highlighting the critical roles of sequential color ordering and contrast, this research proposes AI-friendly cartographic design guidelines. These findings provide empirical evidence and methodological guidance for optimizing map understanding capabilities in artificial intelligence systems, bridging the gap between classical cartography and modern vision-language models.

0 citationsRead paper

FPSGen: Flexible Point Cloud Scene Generation with BEV-Supported Transport Flows

Jul 29, 2026

Existing point cloud scene generation methods rely on partial scans as conditioning inputs, leading to a mismatch between training and inference, poor handling of sparsity in distant regions and occluded areas, and limited flexibility in generating scenes without LiDAR observations. To address these limitations, this work proposes a unified generation framework that dispenses with partial scans by predicting density, height, and occupancy masks in bird’s-eye view (BEV) to construct structured point sources. Furthermore, it introduces a teacher–student approximate optimal transport mechanism that learns straighter transport paths for efficient single-step point generation. The approach supports both unconditional and multi-cue conditional generation, achieving state-of-the-art Jensen–Shannon divergence (JSD) and voxel IoU on SemanticKITTI, and the best Coverage score on KITTI-360 under unconditional generation.

0 citationsRead paper