Pith. sign in

REVIEW 11 cited by

PF-LRM: Pose-Free Large Reconstruction Model for Joint Pose and Shape Prediction

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2311.12024 v2 pith:PZJOYWCM submitted 2023-11-20 cs.CV

Peng Wang , Hao Tan , Sai Bi , Yinghao Xu , Fujun Luan , Kalyan Sunkavalli , Wenping Wang , Zexiang Xu
show 1 more author
Kai Zhang
This is my paper
classification cs.CV
keywords pf-lrmlargemodelreconstructioncameraobjectposepose-free
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

We propose a Pose-Free Large Reconstruction Model (PF-LRM) for reconstructing a 3D object from a few unposed images even with little visual overlap, while simultaneously estimating the relative camera poses in ~1.3 seconds on a single A100 GPU. PF-LRM is a highly scalable method utilizing the self-attention blocks to exchange information between 3D object tokens and 2D image tokens; we predict a coarse point cloud for each view, and then use a differentiable Perspective-n-Point (PnP) solver to obtain camera poses. When trained on a huge amount of multi-view posed data of ~1M objects, PF-LRM shows strong cross-dataset generalization ability, and outperforms baseline methods by a large margin in terms of pose prediction accuracy and 3D reconstruction quality on various unseen evaluation datasets. We also demonstrate our model's applicability in downstream text/image-to-3D task with fast feed-forward inference. Our project website is at: https://totoro97.github.io/pf-lrm .

Discussion (0). Sign in to comment.

Forward citations

Cited by 11 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. CORGI: Consistency-Aware 3D Dog Reconstruction from a Single Image in the Wild

    cs.CV 2026-07 unverdicted novelty 7.0 of 10

    CORGI reconstructs high-fidelity, animatable 3D dogs from a single in-the-wild image via canonical orbital generation, deformable 3DGS anchored to D-SMAL, and self-supervised generative repair, without 3D supervision.

  2. DGS-LRM: Real-Time Deformable 3D Gaussian Reconstruction From Monocular Videos

    cs.GR 2025-06 conditional novelty 7.0 of 10

    A single feed-forward transformer predicts per-pixel deformable 3D Gaussians with dense scene flow from a posed monocular video, enabling real-time dynamic view synthesis and 3D tracking.

  3. GeoWorldAD: Geometry World Action Model for Autonomous Driving

    cs.RO 2026-07 conditional novelty 6.0 of 10

    Grounding an autonomous-driving action model in ego-aligned multi-scale 3D geometry and latent future-geometry tokens improves NAVSIM closed-loop PDMS/EPDMS over prior geometry- and world-model-based planners.

  4. Follow My Hold: Hand-Object Interaction Reconstruction through Geometric Guidance

    cs.CV 2025-08 unverdicted novelty 6.0 of 10

    A diffusion model guided by hand-object interaction and geometric cues reconstructs 3D hand-held object geometry from monocular RGB images.

  5. MagicHOI: Leveraging 3D Priors for Accurate Hand-object Reconstruction from Short Monocular Video Clips

    cs.CV 2025-08 conditional novelty 6.0 of 10

    MagicHOI integrates a novel view synthesis diffusion prior with a visibility-aware weighting strategy to reconstruct accurate hand-object 3D shapes from short monocular videos with partial object visibility.

  6. DualMat: PBR Material Estimation via Coherent Dual-Path Diffusion

    cs.CV 2025-08 conditional novelty 6.0 of 10

    DualMat is a dual-path diffusion model combining an albedo-optimized pretrained latent path with a material-specialized compact latent path, using feature distillation and rectified flow to estimate PBR materials from...

  7. MMIG-Bench: Towards Comprehensive and Explainable Evaluation of Multi-Modal Image Generation Models

    cs.CV 2025-05 conditional novelty 6.0 of 10

    MMIG-Bench is a unified benchmark of 4,850 prompts and 1,750 reference images with a three-level evaluation suite, including the VQA-based Aspect Matching Score that correlates with human ratings.

  8. X-GRM: Large Gaussian Reconstruction Model for Sparse-view X-rays to Computed Tomography

    eess.IV 2025-05 conditional novelty 6.0 of 10

    A large transformer with fixed-voxel Gaussian splatting reconstructs CT volumes from 6-10 X-ray projections in under a second, substantially beating prior sparse-view methods in simulation.

  9. A Red Teaming Framework for Large Language Models: A Case Study on Faithfulness Evaluation

    cs.CL 2026-06 unverdicted novelty 5.0 of 10

    Introduces a multi-role red teaming framework using attacker and jury models that increases attack success rates by up to 7.9% on LLM faithfulness in question-answering tasks.

  10. WorldMirror: Universal 3D World Reconstruction with Any-Prior Prompting

    cs.CV 2025-10 conditional novelty 5.0 of 10

    A unified feed-forward network that ingests optional geometric priors and jointly predicts point maps, depth, camera poses, normals, and 3D Gaussians, reporting state-of-the-art results on multiple 3D benchmarks.

  11. ShapeLLM-Omni: A Native Multimodal LLM for 3D Generation and Understanding

    cs.CV 2025-06 conditional novelty 5.0 of 10

    ShapeLLM-Omni unifies text, image, and 3D generation and understanding in one autoregressive LLM using discrete 3D tokens and a new 3D-Alpaca training dataset.

Pith tools