Monocular 3D object detection methods increasingly rely on depth-guided transformers, but they operate entirely in the perspective image plane where spatial relationships are distorted by foreshortening. We propose MonoMIP, a dual-decoder framework that adds complementary 3D geometric reasoning through Bird's-Eye View (BEV) features constructed via Multi-Height Inverse Perspective Mapping. We perform IPM at K elevation planes spanning ground to rooftop, producing an ordered stack of height-conditioned metric BEV features that reduces above-ground misprojection and exposes depth-dependent appearance variation across planes. A BEV decoder with multi-scale deformable attention aggregates this multi-height evidence per query, while a depth-guided 3D decoder provides complementary perspective-space predictions. Without extra training data, MonoMIP attains 25.84% Moderate AP3D|R40 on KITTI val and 21.66% on the official test set for Car, outperforming recent published monocular methods on both the KITTI and Waymo benchmarks.
CGGT is proposed, a Curve-Grounded Geometry Transformer that directly grounds 3D-consistent 2D curve instances in the image space from sparse, unposed multi-view images, demonstrating its potential for practical CAD-style wireframe reconstruction from unconstrained visual inputs.
Zhirui Gao, Renjiao Yi, Yunfan Ye et al.· 0 citations
GALoc, a geometry-first framework that replaces depth prediction with gravity-aligned wireframes that satisfy verticality and coplanarity by construction, is proposed and evaluated end-to-end on Structured3D, with calibrated noise on Gibson, and on real-world author-collected sequences.
Jeahn Han, Minji Kim, Jeongbin Sohn et al.· 0 citations
Recent advances in 3D Gaussian Splatting have enabled open-vocabulary and referring segmentation by distilling semantic knowledge from 2D foundation models into 3D representations. However, existing referring fields embed language features in a globally view-invariant space, making them fundamentally unable to resolve...
Recent depth foundation models like Depth Anything 3 (DA3) achieve remarkable multi-view depth estimation but assume static 3D scenes, limiting their applicability to real-world dynamic environments. Existing training-free 4D methods like Easi3R and VGGT4D rely on correspondence-trained backbones whose attention encode...
Xin-Hao Xiang, Wei-Yang Li, Zhi-Jie Zheng et al.· 0 citations
Monocular Gaussian SLAM must recover camera motion, surface structure, and appearance from an RGB sequence without metric depth input or benchmark geometry during reconstruction. This setting is challenged by scale-ambiguous predictions, spatially varying reliability, and the tendency of an unconstrained Gaussian map t...
