arXiv Computer Vision
Semantic Reconstruction and 3-D Detection via Learned Multi-Pair Fusion in RF Imaging
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arXiv Computer Vision
Aug 25
Learning Spherical Occupancy Profiles for Multi-View 3D Reconstruction and Generation
arXiv:2608.23206v1 Announce Type: new Abstract: We study spherical occupancy profiles-the ray-wise occupancy probability profiles P(r) = T(r) o(r) distilled from multi-view 3D Gaussian reconstruction...
arXiv Computer Vision
Oct 9
Look Where You Can: Active View Selection for CAD Reconstruction under Occlusion
The paper introduces SightCAD, a framework for parametric CAD reconstruction that explicitly handles view feasibility constraints in realistic indoor scenes. It learns a view selector to choose a small set of feasible viewpoints for a vision‑language model that generates executable CadQuery code, and jointly trains the selector and the VLM using a reward that depends on the final geometric fidelity. The approach outperforms baseline methods on several benchmarks, achieving higher intersection‑over‑union scores and a lower invalid‑code rate.
arXiv Computer Vision
Sep 17
DISTA-Net++: Rethinking Infrared Small Target Unmixing Beyond Sub-Pixel Separation
arXiv:2609.18773v1 Announce Type: new Abstract: Long-range infrared imaging frequently confronts dense target clusters whose diffraction-limited signatures merge into a single indistinguishable blob,...
arXiv Machine Learning
Sep 30
Beyond Missing Rates: Rethinking Incomplete Multi-View Clustering with Protocol Divergence
The paper introduces the concept of protocol divergence, showing that identical nominal missing rates can lead to vastly different learning regimes in incomplete multi‑view clustering. It critiques existing evaluation practices that ignore observation structure and proposes CRAFT, a train‑once framework that fuses observed views with mask‑aware attention, enabling efficient deployment across multiple missing‑view protocols. Experiments on CUB, MultiFashion, and other benchmarks demonstrate CRAFT’s superior performance and significant computational savings through checkpoint reuse.
arXiv Machine Learning
Sep 30
TomoTransformer: Towards a Foundation Model for CT Reconstruction
arXiv:2609.37605v1 Announce Type: cross Abstract: Supervised deep learning has advanced sparse-view tomographic reconstruction. However, conventional models, which typically map filtered back-project...
arXiv AI
Jul 20
Unsupervised Deep Learning for Inverse Problems in Computed Tomography
arXiv:2508. 05321v4 Announce Type: replace-cross Abstract: Assume you encounter an inverse problem that shall be solved for a large number of data, but no ground-truth data is available.