arXiv:2608. 19894v1 Announce Type: new Abstract: Multi-view computer vision pipelines typically rely on accurate sparse keypoints and robust descriptors.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.
arXiv:2601. 18252v2 Announce Type: replace-cross Abstract: Wireframe parsing aims to recover line segments and their junctions to form a structured geometric representation useful for downstream tasks such as Simultaneous Localization and Mapping (SLAM).
CLIDD (Cross-Layer Independent Deformable Description) is a local feature representation method that samples directly from independent feature hierarchies using learnable offsets, avoiding the construction of a dense feature map and thus reducing memory and computation overhead. The approach breaks interpolation-induced correlation through per-keypoint per-layer offsets, preserving discriminability even at high keypoint densities. A hardware-aware kernel fusion strategy and a scalable training framework that combine metric learning and knowledge distillation enable real-time performance, achieving superior matching accuracy and exceeding 200 FPS on edge devices.
The paper introduces Adaptive Dual-Constrained Line Aggregation (ADLA), a framework that extracts line segments across multiple detection paradigms by aggregating pixels from an edge strength map under orientation coherence and bounded orthogonal distance constraints. ADLA dynamically updates line centroids and orientations, incorporates edge strength into various stages, and reduces parameter tuning. Experiments on generic, wireframe, and Manhattan line segment datasets show strong performance, achieving F^H scores of 0.8665, 0.8720, and 0.7297 respectively.
arXiv:2504.07335v4 Announce Type: replace Abstract: We propose DLTPose, a novel method for 6DoF object pose estimation from RGBD images that combines the accuracy of sparse keypoint methods with the...
arXiv:2607.11099v2 Announce Type: replace-cross Abstract: Reliable visual data association is fundamental to visual SLAM (V-SLAM), as it directly determines the quality of the camera pose estimation...
arXiv:2607.09985v2 Announce Type: replace Abstract: Object pose estimation is a fundamental problem in 3D vision. Although recent state-of-the-art approaches achieve strong performance, generalizatio...