一、问题现象(附报错日志上下文): subm_sparse_conv3d.cpp文件中,索引计算函数: aicoreinline void IndicesCompute(int32_t progress, int32_t tensor_size, uint64_t address) 第二个参数是tensor_size,指输入索引的数量; 在__aicore__ inline void Process()中调用IndicesCompute()函数时,传递进去的第二个实...
描述<在这里详细描述你的改动,包括改动的原因和所采取的方法。修复SparseConv3dGrad 算子在 kernel_size和in_channels过小时无法用满核导致出错Bug 关联的I...
VirConv-L: A light-weight multimodal 3D detector based on Virtual Sparse Convolution. VirConv-T: A improved multimodal 3D detector based on Virtual Sparse Convolution and transformed refinement scheme. VirConv-S: A semi-supervised VirConv-T based on pseudo labels and fine-tuning. ...
@inproceedings{focalsconv-chen, title={Focal Sparse Convolutional Networks for 3D Object Detection}, author={Chen, Yukang and Li, Yanwei and Zhang, Xiangyu and Sun, Jian and Jia, Jiaya}, booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition}, year={2022} }...
将mmdet3d/ops/spconv/conv.py中的所有@CONV_LAYERS.register_module()替换成@CONV_LAYERS.register_module(force=True)。 替换前: 替换后: 参考文章: 【深度学习mmdetection错误】——mmdetection 运行报错KeyError:‘ConvWS is already registered in ...
A unified multi-modal fusion framework is proposed to combine image, virtual points and points to achieve more accurate 3D detection.A pixel-voxel sparse convolution is designed to perform feature-level fusion of point clouds and images, effectively realizing voxel-based backbone fusion.A noise-resi...
Focal Sparse Convolutional Networks for 3D Object Detection (CVPR 2022, Oral) - caishuo-C/FocalsConv
However, MLPs struggle to effectively capture the complex spatial relationships inherent in 3D scene data. To address this issue, we propose a novel and efficient framework for 3D instance segmentation called TSPconv-Net. In contrast to existing methods that primarily depend on MLPs for feature ...
However, MLPs struggle to effectively capture the complex spatial relationships inherent in 3D scene data. To address this issue, we propose a novel and efficient framework for 3D instance segmentation called TSPconv-Net. In contrast to existing methods that primarily depend on MLPs for feature ...
Although TSPconv-Net avoids a large amount of meaningless computation through submanifold sparse convolution, its processing speed is still not ideal compared to the original 3D-BoNet. This issue warrants further investigation in future research, aiming to find ways to enhance the network’s ...