Multi-modal Test-time Adaptation for 3D Semantic Segmentation
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Solution Overview
Problem
Existing 3D semantic segmentation models struggle with generalization across different environments and data distributions, leading to poor performance when adapted to new, unseen data without access to source domain data.
Innovation Solution
The method employs multi-modal test-time adaptation by using pre-trained Camera and Lidar Intra-modal Pseudo-label Generators, applying fast and slow 2D and 3D models to input data, fusing pseudo-label predictions through an Inter-modal Pseudo-label Refinement module, and updating model parameters using self-training signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing 3D semantic segmentation models are trained on source domain data, then they achieve good performance on training data, but they fail to generalize to new, unseen data distributions without access to source domain data
Solution Approach 1:
The system pre-trains Camera and Lidar Intra-modal Pseudo-label Generators on source domain data before deployment. These pre-trained generators create pseudo-labels for target domain data, enabling the model to adapt to new environments without requiring source domain data during inference. This preliminary preparation resolves the contradiction by establishing a foundation that enables both reliable performance and adaptability to unseen distributions.
Solution Approach 2:
The patent introduces an Inter-modal Pseudo-label Refinement module that acts as an intermediary between Camera and Lidar modalities. This module fuses pseudo-label predictions from both modalities, creating refined labels that improve generalization to new environments. The intermediary refinement process enables the system to adapt to new data distributions while maintaining reliable performance through cross-modal validation.
2Device complexity
If single-modal pseudo-label generation is used, then the system is simpler, but the pseudo-labels lack robustness and confidence
Solution Approach 1:
The patent merges Camera and Lidar modalities to generate pseudo-labels through the Inter-modal Pseudo-label Refinement module. By combining predictions from both modalities and measuring their consistency, the system produces more robust and confident pseudo-labels. This multi-modal fusion resolves the contradiction by demonstrating that the increased structural complexity directly improves pseudo-label reliability through cross-modal validation.
3Productivity
If fast models are used for pseudo-label generation, then processing speed is improved, but prediction accuracy and confidence are reduced
Solution Approach 1:
The patent implements a dynamic pseudo-label generation strategy that adapts to each test sample. The system measures prediction consistency between fast and slow models for each sample and selectively uses pseudo-labels based on confidence thresholds. This dynamic approach resolves the contradiction by allowing the system to use fast models for most samples while leveraging slow models for uncertain cases, optimizing both speed and accuracy on a per-sample basis.
Data Source
AI summary
Systems and methods are provided for multi-modal test-time adaptation. The method includes inputting a digital image into a pre-trained Camera Intra-modal Pseudo-label Generator, and inputting a point cloud set into a pre-trained Lidar Intra-modal Pseudo-label Generator. The method further includes applying a fast 2-dimension (2D) model, and a slow 2D model, to the inputted digital image to apply pseudo-labels, and applying a fast 3-dimension (3D) model, and a slow 3D model, to the inputted point cloud set to apply pseudo-labels. The method further includes fusing pseudo-label predictions from the fast models and the slow models through an Inter-modal Pseudo-label Refinement module to obtain robust pseudo labels, and measuring a prediction consistency for the pseudo-labels. The method further includes selecting confident pseudo-labels from the robust pseudo labels and measured prediction consistencies to form a final cross-modal pseudo-label set as a self-training signal, and updating batch parameters utilizing the self-training signal.