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Found 8 papers, 3 papers with code
Distilling Multi-modal Large Language Models for Autonomous Driving
Training with DiMA results in a 37% reduction in the L2 trajectory error and an 80% reduction in the collision rate of the vision-based planner, as well as a 44% trajectory error reduction in longtail scenarios.
Multimodal 3D Object Detection on Unseen Domains
To this end, we propose CLIX$^\text{3D}$, a multimodal fusion and supervised contrastive learning framework for 3D object detection that performs alignment of object features from same-class samples of different domains while pushing the features from different classes apart.
Equivariant Spatio-Temporal Self-Supervision for LiDAR Object Detection
This can enable improved performance in downstream tasks that are equivariant to such transformations.
MonoDiff: Monocular 3D Object Detection and Pose Estimation with Diffusion Models
Hence we adopt a Gaussian mixture model to sample noise during the forward diffusion process and initialize the reverse diffusion process.
CLIP goes 3D: Leveraging Prompt Tuning for Language Grounded 3D Recognition
Attempting to train the visual and text encoder to account for this shift results in catastrophic forgetting and a notable decrease in performance.
Attentive Prototypes for Source-free Unsupervised Domain Adaptive 3D Object Detection
We demonstrate our approach on two recent object detectors and achieve results that out-perform the other domain adaptation works.
Uncertainty-aware Mean Teacher for Source-free Unsupervised Domain Adaptive 3D Object Detection
Pseudo-label based self training approaches are a popular method for source-free unsupervised domain adaptation.
Lidar Light Scattering Augmentation (LISA): Physics-based Simulation of Adverse Weather Conditions for 3D Object Detection
Lidar-based object detectors are critical parts of the 3D perception pipeline in autonomous navigation systems such as self-driving cars.
