Comments (2)
Referenced from his paper:
"On a NVIDIA Tesla V100 GPU, the inference time of DSGN for one image pair is 0.682s on average, where 2D feature extraction for left and right images takes 0.113s, constructing the plane-sweep volume and 3D geometric volume takes 0.285s, and 3D object detection on 3D geometric volume takes 0.284s. The computation bottleneck of DSGN lies on 3D convolution layers."
from dsgn.
thanks, I just saw it.
from dsgn.
Related Issues (20)
- Train, Val, Test split HOT 4
- Inference Speed HOT 2
- 同一个目标多个预测框 HOT 9
- the error of the depth estimation HOT 2
- What is the supervision of point cloud? HOT 6
- may i train this model without the depth estimation? HOT 1
- CUDA Of Memory HOT 11
- monocular experiment HOT 12
- Model that has been trained
- tensorflow / tensorflow lite HOT 2
- About the construction of Plane Sweep Volume HOT 4
- where's the split files?
- 作者你好,你的build_cost_volume_forward部分是c++实现的吗? HOT 1
- TypeError: expected dtype object, got 'numpy.dtype[float64]' in eval.py HOT 1
- ./configs/default/config_disp.py这个文件是用来做什么的,是只包含深度估计网络的config吗 HOT 1
- Question about setup(performance issue) HOT 2
- Using 3D Geometry Volume to predict depth. HOT 4
- Results don't match with paper on KITTI Leaderboard HOT 7
- CUDA out of memory HOT 7
- The wrong outputs of the pre-trained model HOT 7
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