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Found 7 papers, 7 papers with code
SPOT: Scalable 3D Pre-training via Occupancy Prediction for Autonomous Driving
Our contributions are threefold: (1) Occupancy prediction is shown to be promising for learning general representations, which is demonstrated by extensive experiments on plenty of datasets and tasks.
ReSimAD: Zero-Shot 3D Domain Transfer for Autonomous Driving with Source Reconstruction and Target Simulation
Domain shifts such as sensor type changes and geographical situation variations are prevalent in Autonomous Driving (AD), which poses a challenge since AD model relying on the previous domain knowledge can be hardly directly deployed to a new domain without additional costs.
AD-PT: Autonomous Driving Pre-Training with Large-scale Point Cloud Dataset
It is a long-term vision for Autonomous Driving (AD) community that the perception models can learn from a large-scale point cloud dataset, to obtain unified representations that can achieve promising results on different tasks or benchmarks.
Uni3D: A Unified Baseline for Multi-dataset 3D Object Detection
In this paper, we study the task of training a unified 3D detector from multiple datasets.
Bi3D: Bi-domain Active Learning for Cross-domain 3D Object Detection
Unsupervised Domain Adaptation (UDA) technique has been explored in 3D cross-domain tasks recently.
UniDA3D: Unified Domain Adaptive 3D Semantic Segmentation Pipeline
Different from previous studies that only focus on a single adaptation task, UniDA3D can tackle several adaptation tasks in 3D segmentation field, by designing a unified source-and-target active sampling strategy, which selects a maximally-informative subset from both source and target domains for effective model adaptation.
Learning Cross-Image Object Semantic Relation in Transformer for Few-Shot Fine-Grained Image Classification
Few-shot fine-grained learning aims to classify a query image into one of a set of support categories with fine-grained differences.
