3D-VCD reduces hallucinations in 3D-LLM embodied agents by contrasting predictions from original and distorted 3D scene representations at inference time.
Uni3D-LLM: unifying point cloud perception, generation and editing with large language models
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.CV 3years
2026 3representative citing papers
Pointy, a lightweight transformer trained on 39k point clouds, outperforms larger foundation models trained on 200k+ samples and nears SOTA from million-sample multimodal models.
SGSoft introduces a template-guided pipeline that fuses semantic and geometric features to learn dense correspondences across deformable 3D shapes with claimed SOTA generalization and real-time efficiency.
citing papers explorer
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3D-VCD: Hallucination Mitigation in 3D-LLM Embodied Agents through Visual Contrastive Decoding
3D-VCD reduces hallucinations in 3D-LLM embodied agents by contrasting predictions from original and distorted 3D scene representations at inference time.
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Pointy - A Lightweight Transformer for Point Cloud Foundation Models
Pointy, a lightweight transformer trained on 39k point clouds, outperforms larger foundation models trained on 200k+ samples and nears SOTA from million-sample multimodal models.
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SGSoft: Learning Fused Semantic-Geometric Features for 3D Shape Correspondence via Template-Guided Soft Signals
SGSoft introduces a template-guided pipeline that fuses semantic and geometric features to learn dense correspondences across deformable 3D shapes with claimed SOTA generalization and real-time efficiency.