in two main stages: one that encodes the scene into a canonical space and another that maps this canonical representation into the deformed scene at a particular time. Both mappings are simultaneously learned using fully-connected networks. Once the networks are trained, D-NeRF can render novel i...
3DGS大规模数据集与场景理解 ShapeSplat: A Large-scale Dataset of Gaussian Splats and Their Self-Supervised Pretraining https://arxiv.org/abs/2408.10906 Qi Ma, Yue Li, Bin Ren, Nicu Sebe, Ender Konukoglu, Theo Gevers, Luc Van Gool, Danda Pani Paudel ETH Zurich、Sofia University、University ...
python eval.py \ --base_folder $EXPERIMENT_PATH \ --gin_bindings="data_dir='$DATASET_PATH'" \ --gin_configs $CONFIG_PATH We also provide an example script at train_eval_balloon.sh. Configuration Similiar to HyperNeRF, We use Gin for configuration. We provide a couple of preset config...
Last, we find that our approach has merit in sparse-view settings beyond those in the OR, by benchmarking on the NVS-RGBD dataset that contains as few as three training views. NeRF-OR synthesizes images with a PSNR of 26.72dB, a 1.7% improvement over state of the art.#Our results ...
Q: The NeRF reconstruction of my custom dataset looks bad; what can I do?A: There could be multiple issues:COLMAP might have been unable to reconstruct camera poses. There might have been movement or blur during capture. Don't treat capture as an artistic task; treat it as photogrammetry....
虽然360dataset 中包含很多具有挑战性的场景内容,它不能衡量以渲染质量作为规模的函数,因为这个数据集是由相机环绕在一个中心对象以大致恒定的距离拍摄得到的,学习模型不需要处理训练在不同的图像分辨率或不同的距离中心对象。因此研究者使用一个更具...
datasets.base_dataset import BaseDataset from common.utils.cfgs_utils import valid_key_in_cfgs from common.utils.img_utils import img_scale, read_img from common.utils.torch_utils import np_wrapper class Base3dDataset(BaseDataset): 2 changes: 1 addition & 1 deletion 2 arcnerf/datasets/base...
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For the DTU dataset, you might further need to adjust the Config.dtu_mask_path argument. The *_bl.gin configs are the mip-NeRF baseline configurations. Once the training process is started, you can monitor the progress via the tensorboard by calling tensorboard --logdir={LOGDIR} and...
NeRSemble dataset SUBJECT="074" python vhap/combine_nerf_datasets.py \ --src_folders \ export/nersemble/${SUBJECT}_EMO-1_v16_DS4_whiteBg_staticOffset_maskBelowLine \ export/nersemble/${SUBJECT}_EMO-2_v16_DS4_whiteBg_staticOffset_maskBelowLine \ export/nersemble/${SUBJECT}_EMO-3_v16_DS4...