Brief

Researchers have introduced TriMM, a novel feed-forward generative model designed for high-quality 3D asset creation. TriMM uniquely integrates features from multiple modalities, such as RGB images, RGBD data, and point clouds, to enhance both texture and geometric detail in generated 3D assets. The model employs collaborative multi-modal coding to preserve the distinct strengths of each data type and utilizes auxiliary 2D and 3D supervision to improve robustness. Experiments show that TriMM can achieve competitive performance with significantly less training data compared to existing models. AI

Researchers have developed a deep learning framework to enable autonomous logistics vehicles to detect and estimate the pose of load carriers. The system utilizes a convolutional neural network that processes RGBD data to identify specific landmarks on the carriers. By combining these inferred landmarks with geometric information, the network accurately determines the carrier's position and orientation, proving effective for intralogistics applications. AI