arXiv:2605. 26702v2 Announce Type: replace-cross Abstract: Reliable watermarking of panoramic imagery is fundamentally challenged by arbitrary 3D rotations.
By Pengzhen Chen, Yanwei Liu, Xiaoyan Gu, Antonios Argyriou, Wu Liu, Weiping Wang
arXiv:2606. 27864v1 Announce Type: cross Abstract: Vision transformers have become a dominant architecture for visual recognition.
By T\={\i}kun \^Ong, Georg B\"okman
arXiv:2608.31045v1 Announce Type: new
Abstract: Rotational symmetry is one of the most important structural principles in machine learning on 3D data. In applications ranging from physics and materia...
By Peter Lippmann, Fred A. Hamprecht
arXiv:2609.23182v1 Announce Type: new
Abstract: Feed-forward 3D Gaussian Splatting now reconstructs renderable scenes from unposed, uncalibrated images. Yet, most models supervise only photometric co...
By Si-Yu Lu, Yung-Yao Chen, Yi Jan Chen, Shang-Lin Li, Ching-Chan Liao, Wen-Huang Cheng
The paper introduces an observation‑Gram matrix that captures how each Gaussian in a 3D Gaussian Splatting model is viewed from training camera directions. This matrix serves as a distortion metric, enabling closed‑form degree reduction, Lagrangian rate‑distortion degree allocation, and matrix‑weighted vector quantisation. When applied to the Compressed3D framework, the metric improves PSNR by 0.49 dB before fine‑tuning and still yields a 0.32 dB gain at matched bitrate without any training images, while a training‑free stack built on the metric is 15% smaller than the image‑free GSICO at equal quality on Mip‑NeRF 360.
By Krzysztof Pietroszek
arXiv:2606. 04108v1 Announce Type: cross Abstract: Single-view 3D generative models have achieved impressive visual quality, yet they are not designed to satisfy structural or functional requirements, and in practice, often fall short.
By Guangda Ji, Qimin Chen, Qinchan Li, Mingrui Zhao, Kai Wang, Hao Zhang