PAR3D is a part-aware 3D-MLLM framework with ScenePart dataset, Part-Aware 3D Representation Learning, and Hierarchical Segmentation Query Generation to improve part-level 3D scene understanding.
arXiv preprint arXiv:2510.23607 (2025)
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Chorus pretrains a shared 3D Gaussian scene encoder via multi-teacher distillation to capture holistic features from high-level semantics to fine-grained structure, with strong transfer on segmentation and point-cloud tasks using far fewer scenes.
DeWorldSG improves 3D scene graph generation from RGB-D sequences by using depth-guided 3D Gaussian object nodes and V-JEPA 2 world-model priors for spatiotemporal relation refinement, reporting large recall gains on 3DSSG and ReplicaSSG.
PointACT proposes a 3D-aware dual-system VLA policy using multi-scale point-action interaction with bottleneck window self-attention, achieving 10% higher success rates on RLBench-10Tasks over prior pretrained VLAs.
Gaussian and related cropping strategies for point cloud subclouds improve 3D neural network performance over spherical cropping on large outdoor scenes.
PASR performs pose-aware analysis-by-synthesis by aligning 3D projections with DINOv3 patch features, outperforming prior methods on clean and occluded retrieval while also handling pose estimation and classification.
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