RDMNet: Reliable Dense Matching Based Point Cloud Registration for Autonomous Driving

Point cloud registration is an important task in robotics and autonomous driving to estimate the ego-motion of the vehicle. Recent advances following the coarse-to-fine manner show promising potential in point cloud registration. However, existing methods rely on good superpoint correspondences, which are hard to be obtained reliably and efficiently, thus resulting in less robust and accurate point cloud registration. In this paper, we propose a novel network, named RDMNet, to find dense point correspondences coarse-to-fine and improve final pose estimation based on such reliable correspondences. Our RDMNet uses a devised 3D-RoFormer mechanism to first extract distinctive superpoints and generates reliable superpoints matches between two point clouds. The proposed 3D-RoFormer fuses 3D position information into the transformer network, efficiently exploiting point clouds' contextual and geometric information to generate robust superpoint correspondences. RDMNet then propagates the sparse superpoints matches to dense point matches using the neighborhood information for accurate point cloud registration. We extensively evaluate our method on multiple datasets from different environments. The experimental results demonstrate that our method outperforms existing state-of-the-art approaches in all tested datasets with a strong generalization ability.Comment: 11 pages, 9 figure

Discrete social recommendation

National Research Foundation (NRF) Singapore under its AI Singapore Programm

Compositional coding for collaborative filtering

National Research Foundation (NRF) Singapore under its AI Singapore Programm

Global systematic review with meta-analysis shows that warming effects on terrestrial plant biomass allocation are influenced by precipitation and mycorrhizal association

Biomass allocation in plants is fundamental for understanding and predicting terrestrial carbon storage. Yet, our knowledge regarding warming effects on root: shoot ratio (R/S) remains limited. Here, we present a meta-analysis encompassing more than 300 studies and including angiosperms and gymnosperms as well as different biomes (cropland, desert, forest, grassland, tundra, and wetland). The meta-analysis shows that average warming of 2.50 °C (median = 2 °C) significantly increases biomass allocation to roots with a mean increase of 8.1% in R/S. Two factors associate significantly with this response to warming: mean annual precipitation and the type of mycorrhizal fungi associated with plants. Warming-induced allocation to roots is greater in drier habitats when compared to shoots (+15.1% in R/S), while lower in wetter habitats (+4.9% in R/S). This R/S pattern is more frequent in plants associated with arbuscular mycorrhizal fungi, compared to ectomycorrhizal fungi. These results show that precipitation variability and mycorrhizal association can affect terrestrial carbon dynamics by influencing biomass allocation strategies in a warmer world, suggesting that climate change could influence belowground C sequestration

SOL: A library for scalable online learning algorithms

Singapore National Research Foundatio

A robust and active hybrid catalyst for facile oxygen reduction in solid oxide fuel cells

The sluggish oxygen reduction reaction (ORR) greatly reduces the energy efficiency of solid oxide fuel cells (SOFCs). Here we report our findings in dramatically enhancing the ORR kinetics and durability of the state-of-the-art La[subscript 0.6]Sr[subscript 0.4]Co[subscript 0.2]Fe[subscript 0.8]O[subscript 3](LSCF) cathode using a hybrid catalyst coating composed of a conformal PrNi[subscript 0.5]Mn[subscript 0.5]O[subscript 3](PNM) thin film with exsoluted PrOxnanoparticles. At 750°C, the hybrid catalyst-coated LSCF cathode shows a polarization resistance of ∼0.022 Ω cm[superscript 2], about 1/6 of that for a bare LSCF cathode (∼0.134 Ω cm[superscript 2]). Further, anode-supported cells with the hybrid catalyst-coated LSCF cathode demonstrate remarkable peak power densities (∼1.21 W cm[superscript -2]) while maintaining excellent durability (0.7 V for ∼500 h). Near Ambient X-ray Photoelectron Spectroscopy (XPS) and Near Edge X-Ray Absorption Fine Structure (NEXAFS) analyses, together with density functional theory (DFT) calculations, indicate that the oxygen-vacancy-rich surfaces of the PrOxnanoparticles greatly accelerate the rate of electron transfer in the ORR whereas the thin PNM film facilitates rapid oxide-ion transport while drastically enhancing the surface stability of the LSCF electrode