Monocular temporal 3D detection aims to detect objects in 3D, given a monocular video. Query-based 3D detectors unify detection and cross-view association, but their learnable queries fit the spatial distribution of the training data (e.g., field-of-view). We show that this issue is especially severe when these models are applied to monocular video, hindering generalization to unseen datasets and environments. To address this limitation, we introduce MAGneT-3D, the first method for domain-generalized monocular temporal 3D object detection. Instead of relying on static learnable queries, we propose a Domain-Robust Anchor Generator (DRAG) approach that adaptively derives 3D proposals during inference. To further enable domain generalization, we propose a Temporal Refinement and Identity Merging (TRIM) strategy, reducing dependence on specific 3D proposals. To enable comprehensive domain-generalization evaluation, we establish a cross-dataset benchmark spanning nuScenes, Waymo, Lyft, and ONCE. Under zero-shot domain shifts, MAGneT-3D outperforms all baselines, improving NDS from 12.1% to 18.6% while also increasing in-domain accuracy.
Map-Det3D is an online multi-view 3D object detection model that brings detection directly into a 3D space reconstructed from RGB, suggesting that training reconstruction priors for detection is a practical route to stable metric 3D detection from monocular video.
Yung-Hsu Yang, Luigi Piccinelli, S. R. Bulò et al.· 1 citation
Results from an adaptation of DINOv3 to the instance-level fusion pipeline of IIFNet3D are interpreted as evidence for a controlled transfer recipe rather than as a causal estimate of the individual contributions of VAID or depth filtering.
Lin-Man Wang, Zi-Fei Zhang, Chun-Ran Zheng et al.· 0 citations
Qwen-3D is introduced, a geometry-aware LMM that compresses visual information within the Qwen backbone using multi-view geometric cues, enabling efficient long-horizon visual reasoning over static scenes and incorporates a query-based segmentation decoder that grounds language directly in the underlying 3D scene repre...
Lucy Lin, Ayush Jain, Yifan Liu et al.· 3 citations
Monocular 3D object detection (M3OD) is attractive for autonomous driving and resource-constrained robotic platforms due to its low hardware cost. While temporal cues can alleviate depth ambiguity, dense feature matching and explicit motion estimation introduce substantial latency and limit low-latency deployment. To a...
Jia-Ying Li, Tong Liu, Xiao-Dong Guo et al.· IEEE Robotics and Automation...· 0 citations
This work proposes a framework for detailed object-level reasoning across multiple objects with three components: MO3D, an instruction dataset requiring fine-grained multi-object comparison; Multi-3DLLM, using a minimal Patch-Interaction Transformer (PIT) that models inter-/intra-object relationships while preserving l...
K. Ide, Ryousuke Yamada, Yue Qiu et al.· 2026 IEEE/CVF Conference on...· 1 citation· ⚡1
OV3D-Bench is introduced, a diagnostic benchmark that compares open-vocabulary monocular 3D detectors under deployment-realistic conditions across seven indoor and outdoor datasets, and indicates that geometric localization is more mature, while open-vocabulary semantics remains the primary bottleneck.
M. Gladkova, Neehar Peri, Ishan Khatri et al.· 0 citations
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