LRM is a large transformer that predicts a NeRF directly from a single image after training on a million-object multi-view dataset.
arXiv preprint arXiv:2304.10261 (2023)
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Flux-GS is a mobile-optimized 3D Gaussian Splatting method that compresses specular energy via Monte Carlo aggregation, recovers details with attribute-conditioned SH offsets, and uses multi-view guidance for densification to cut parameters while keeping visual quality.
CoIn introduces a multi-stage pipeline using diffusion models for initial 2D inpainting, Reference Adaptive GS for 3D reconstruction, and GS-based warping plus a discriminator for multi-view consistent 3D scene inpainting supporting removal and insertion.
AHCQ-SAM introduces ACNR, HLUQ, CAG, and LNQ quantization techniques that deliver 15.2% mAP gain on 4-bit SAM-B and 14.01% J&F gain on 4-bit SAM2-Tiny versus prior PTQ methods.
MobileSAM is a 60x smaller distilled version of SAM that matches original performance and runs 5x faster than concurrent FastSAM while supporting CPU inference.
citing papers explorer
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LRM: Large Reconstruction Model for Single Image to 3D
LRM is a large transformer that predicts a NeRF directly from a single image after training on a million-object multi-view dataset.
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Monte Carlo Energy Aggregation for Mobile 3D Gaussian Splatting
Flux-GS is a mobile-optimized 3D Gaussian Splatting method that compresses specular energy via Monte Carlo aggregation, recovers details with attribute-conditioned SH offsets, and uses multi-view guidance for densification to cut parameters while keeping visual quality.
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CoIn: Comprehensive 2D-3D Inpainting with Gaussian Splatting Guidance
CoIn introduces a multi-stage pipeline using diffusion models for initial 2D inpainting, Reference Adaptive GS for 3D reconstruction, and GS-based warping plus a discriminator for multi-view consistent 3D scene inpainting supporting removal and insertion.
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AHCQ-SAM: Toward Accurate and Hardware-Compatible Post-Training Segment Anything Model Quantization
AHCQ-SAM introduces ACNR, HLUQ, CAG, and LNQ quantization techniques that deliver 15.2% mAP gain on 4-bit SAM-B and 14.01% J&F gain on 4-bit SAM2-Tiny versus prior PTQ methods.
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Faster Segment Anything: Towards Lightweight SAM for Mobile Applications
MobileSAM is a 60x smaller distilled version of SAM that matches original performance and runs 5x faster than concurrent FastSAM while supporting CPU inference.
- On Efficient Variants of Segment Anything Model: A Survey