NoPo4D is the first feed-forward system for dynamic 4D Gaussian splatting from unposed multi-view videos, using velocity decomposition supervised by optical flow and a bidirectional motion encoder.
arXiv preprint arXiv:2512.13689 (2025) 21
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RelFlexformers enable flexible integrable 3D RPE in attention via NU-FFT, generalizing prior methods to heterogeneous token positions with O(L log L) complexity.
TORA distills topological structure from pretrained 3D encoders into flow-matching backbones via cosine matching and CKA loss, delivering up to 6.9x faster convergence and better accuracy on 3D shape assembly benchmarks with zero inference overhead.
A minimally modified vanilla Transformer called Volt achieves state-of-the-art 3D semantic and instance segmentation by using volumetric tokens, 3D rotary embeddings, and a data-efficient training recipe that scales better than domain-specific backbones.
Gaussian and related cropping strategies for point cloud subclouds improve 3D neural network performance over spherical cropping on large outdoor scenes.
citing papers explorer
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No Pose, No Problem in 4D: Feed-Forward Dynamic Gaussians from Unposed Multi-View Videos
NoPo4D is the first feed-forward system for dynamic 4D Gaussian splatting from unposed multi-view videos, using velocity decomposition supervised by optical flow and a bidirectional motion encoder.
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RelFlexformer: Efficient Attention 3D-Transformers for Integrable Relative Positional Encodings
RelFlexformers enable flexible integrable 3D RPE in attention via NU-FFT, generalizing prior methods to heterogeneous token positions with O(L log L) complexity.
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TORA: Topological Representation Alignment for 3D Shape Assembly
TORA distills topological structure from pretrained 3D encoders into flow-matching backbones via cosine matching and CKA loss, delivering up to 6.9x faster convergence and better accuracy on 3D shape assembly benchmarks with zero inference overhead.
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Volume Transformer: Revisiting Vanilla Transformers for 3D Scene Understanding
A minimally modified vanilla Transformer called Volt achieves state-of-the-art 3D semantic and instance segmentation by using volumetric tokens, 3D rotary embeddings, and a data-efficient training recipe that scales better than domain-specific backbones.
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From Spherical to Gaussian: A Comparative Analysis of Point Cloud Cropping Strategies in Large-Scale 3D Environments
Gaussian and related cropping strategies for point cloud subclouds improve 3D neural network performance over spherical cropping on large outdoor scenes.