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View Code? Open in Web Editor NEWDH3D: Deep Hierarchical 3D Descriptors for Robust Large-Scale 6DOF Relocalization
Home Page: https://vision.in.tum.de/research/vslam/dh3d
License: Apache License 2.0
DH3D: Deep Hierarchical 3D Descriptors for Robust Large-Scale 6DOF Relocalization
Home Page: https://vision.in.tum.de/research/vslam/dh3d
License: Apache License 2.0
Thanks for your attention
As stated in the title, I found two model files included in the source directory. One is labelled local and the other global. I know the network is trained in three stages but if I am going to calculate local and global descriptors (also salient points) in a single forward pass, which one should I use? I.e. are both files complete models or only the global one is complete?
Thanks for the great work
I have a question regarding network structure: is it possible to change to input point number without re-training the network from scratch? Thanks
In my case, I am using NVIDIA Geforce RTX 2080.
But RTX 2080 only supports CUDA 10.0 above.
tensorflow/tensorflow#22900
But the tensorflow v1.9.0, v1.10.0 that you used only supports CUDA 9.0.
https://www.tensorflow.org/install/source#tested_build_configurations
Would the replacement of GPU be the only option to train DH3D?
Hello,
did you also validate the point cloud retrieval for DSO? As far as I can see in the paper for DSO only the point cloud registration is validated.
Regards
0%| |0/2073[00:00<?,?it/s] 2021-05-20 10:48:44.249488: E tensorflow/stream_executor/cuda/cuda_blas.cc:647] failed to run cuBLAS routine cublasSgemmBatched: CUBLAS_STATUS_EXECUTION_FAILED
2021-05-20 10:48:44.249528: E tensorflow/stream_executor/cuda/cuda_blas.cc:2505] Internal: failed BLAS call, see log for details
2021-05-20 10:48:44.887998: I tensorflow/stream_executor/stream.cc:4817] stream 0x55c2525213f0 did not memzero GPU location; source: 0x7f1da27fab50
2021-05-20 10:48:44.888031: I tensorflow/stream_executor/stream.cc:4817] stream 0x55c2525213f0 did not memzero GPU location; source: 0x7f1da27fab70
2021-05-20 10:48:44.888071: E tensorflow/stream_executor/cuda/cuda_dnn.cc:2833] failed to enqueue forward batch normalization on stream: CUDNN_STATUS_EXECUTION_FAILED
InternalError (see above for traceback): Blas xGEMMBatched launch failed : a.shape=[10,512,3], b.shape=[10,3,3], m=512, n=3, k=3, batch_size=10
[[Node: MatMul = BatchMatMul[T=DT_FLOAT, adj_x=false, adj_y=false, _device="/job:localhost/replica:0/task:0/device:GPU:0"](split, _arg_R_0_0/_17)]]
I used cuda9.0 and tensorflow1.9, ubuntu18.04,Can you take a look at it for me?
I am curious to hear about the runtime and hardware that you used. You have stated in the paper that for a point cloud with 8192 points, one forward pass took 80ms but I did not see any hardware specifications, or information about the training time, hardware and parameters.
Another question concerns the point-cloud-based relocalization process itself. I know about implementing image-based relocalization, and will be very glad to hear how you are implementing the point-cloud-based relocalization.
Much appreciated! Thanks!
Hi, thank you for the great work!
I intend to modify the code and run it using the tf.Session
equivalent in Tensorflow C++ API.
From the function compute_local
in model.py, I see that the pointcloud input for the input dict is points
, which is derived from
points = tf.concat(pcdset, 0, name='pointclouds')
in the function build_graph
.
However, when I import the meta graph and list the nodes, I am unable to find any node named pointclouds
.
May I know how I can access it and similarly the output node names when I run my session? Apologies if my description sounds confusing as I am quite new to this.
Hi JuanDuGit,
I run the test code to compute the recall but it’s recall performance is different from the paper.
According to the DH3D paper, recall @1 and @1% are 74.16, 85.30.
However, when I ran the globaldesc_extract.py with your pertained model which is in model/global/xxx ,
I got the following results:
Avg_recall :
1 : 0.7532
2 : 0.8284
3 : 0.8624
…
Avg_one_percent_retrieved :
0.8849
I would very much appreciate it if you could give me an explanation about these results. Thank you.
Best,
Ganghee
Is this monocular localization or only lidar based?
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