84 research outputs found

    Rethinking Pseudo-LiDAR Representation

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    The recently proposed pseudo-LiDAR based 3D detectors greatly improve the benchmark of monocular/stereo 3D detection task. However, the underlying mechanism remains obscure to the research community. In this paper, we perform an in-depth investigation and observe that the efficacy of pseudo-LiDAR representation comes from the coordinate transformation, instead of data representation itself. Based on this observation, we design an image based CNN detector named Patch-Net, which is more generalized and can be instantiated as pseudo-LiDAR based 3D detectors. Moreover, the pseudo-LiDAR data in our PatchNet is organized as the image representation, which means existing 2D CNN designs can be easily utilized for extracting deep features from input data and boosting 3D detection performance. We conduct extensive experiments on the challenging KITTI dataset, where the proposed PatchNet outperforms all existing pseudo-LiDAR based counterparts. Code has been made available at: https://github.com/xinzhuma/patchnet.Comment: ECCV2020. Supplemental Material attache

    Effect of simulated acid rain on COâ‚‚, CHâ‚„ and Nâ‚‚O fluxes and rice productivity in a subtropical Chinese paddy field

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    The need of more food production, an increase in acidic deposition and the large capacity of paddy to emit greenhouse gases all coincide in several areas of China. Studying the effects of acid rain on the emission of greenhouse gases and the productivity of rice paddies are thus important, because these effects are currently unknown. We conducted a field experiment for two rice croppings (early and late paddies independent experiment) to determine the effects of simulated acid rain (control, normal rain, and treatments with rain at pH of 4.5, 3.5 and 2.5) on the fluxes of COâ‚‚, CHâ‚„ and Nâ‚‚O and on rice productivity in subtropical China. Total COâ‚‚ fluxes at pHs of 4.5, 3.5 and 2.5 were 10.3, 9.7 and 3.2% lower in the early paddy and 28.3, 14.8 and 6.8% lower in the late paddy, respectively, than the control. These differences from the control were significant for pH 3.5 and 4.5. Total CHâ‚„ fluxes at pHs of 4.5, 3.5 and 2.5 were 50.4, 32.9 and 25.2% lower in the early paddy, respectively, than the control. pH had no significant effect on CHâ‚„ flux in the late paddy or for total (early + late) emissions. Nâ‚‚O flux was significantly higher at pH 2.5 than 3.5 and 4.5 but did not differ significantly from the flux in the control. Global-warming potentials (GWPs) were lower than the control at pH 3.5 and 4.5 but not 2.5, whereas rice yield was not appreciably affected by pH. Acid rain (between 3.5 and 4.5) may thus significantly affect greenhouse gases emissions by altering soil properties such as pH and nutrient pools, whereas highly acidic rain (pH 2.5) could increase GWPs (but not significantly), probably partially due to an increase in the production of plant litter
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