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Found 6 papers, 3 papers with code
BEV@DC: Bird's-Eye View Assisted Training for Depth Completion
In practice, the proposed BEV@DC model comprehensively takes advantage of LiDARs with rich geometric details in training, employing an enhanced depth completion manner in inference, which takes only images (RGB and depth) as input.
Geometry-Aware Network for Domain Adaptive Semantic Segmentation
Moreover, to improve the 2D classifier in the target domain, we perform domain-invariant geometric adaptation from source to target and unify the 2D semantic and 3D geometric segmentation results in two domains.
X-Trans2Cap: Cross-Modal Knowledge Transfer using Transformer for 3D Dense Captioning
Thus, a more faithful caption can be generated only using point clouds during the inference.
Comprehensive Visual Question Answering on Point Clouds through Compositional Scene Manipulation
To tackle this problem, we propose the CLEVR3D, a large-scale VQA-3D dataset consisting of 171K questions from 8, 771 3D scenes.
Medical-VLBERT: Medical Visual Language BERT for COVID-19 CT Report Generation With Alternate Learning
Medical imaging technologies, including computed tomography (CT) or chest X-Ray (CXR), are largely employed to facilitate the diagnosis of the COVID-19.
InstanceRefer: Cooperative Holistic Understanding for Visual Grounding on Point Clouds through Instance Multi-level Contextual Referring
Compared with the visual grounding on 2D images, the natural-language-guided 3D object localization on point clouds is more challenging.
