3 accepted papers
Semantic occupancy has emerged as a powerful representation in world models for its ability to capture rich spatial semantics. However, most existing occupancy world models rely on static and fixed embeddings or grids, which inherently limit the flexibility of perception. Moreover, their ``in-place
As scene coordinate regression (SCoRe) methods become prevailing in the area of visual camera localization, the issue of repetitive or sparse texture scenes continues to be a concern. Specifically, they will suffer from performance degeneration due to ambiguous patterns caused by visual similarity.
In this work, we present PANet, a pixel-level attention network with embedding vector features, which addresses the challenge of 6D pose estimation from a single RGBD image under severe occlusion. PANet produces pixel-wise attention for strong representation learning and leverages a novel selection