Guide-yakabatana mibvunzo · Pakati Nhanho

Point Cloud Processing Quiz

Ongorora kuti point-cloud masisitimu anochengetedza sei isina kurongeka 3D geometry uchichenesa zvikero uye maficha ekudzidza pazvikero zvakawanda zvepasi.

Nzira dzekutungamira dzinoenderanaPoint Cloud Processing
Mubvunzo 1 ye 8

Chii chinonzi point cloud?

Tarisa mubvunzo wese8 mibvunzo

Mhinduro uye tsananguro zvinoonekwa paunenge uchitora bvunzo.

  1. Chii chinonzi point cloud?
    • Muunganidzwa wemapoinzi e3D anotsanangura nzvimbo dziri muchadenga
    • A 2D gidhi yemapikisi eruvara
    • A cluster of servers processing graphics
    • Mepu yemamiriro ekunze ye cloud cover
  2. Nei isingakwanise kuisirwa mufananidzo wakajairwa CNNs wakananga kumakore?
    • Makore madiki madiki
    • Mapoinzi haana kurongeka uye haana giredhi yenguva dzose
    • Point cloud haina ruzivo rwenzvimbo
    • CNNs inoshanda chete mutema nemuchena
  3. Ndeipi yakakosha pfuma inofanirwa kuremekedza gore network?
    • Kusiyana kwemavara
    • Kuodha kubva kuruboshwe kuenda-kurudyi kuverenga
    • Permutation invariance to point ordering
    • Fixed image resolution
  4. Ko PointNet yakawana sei kurongeka-kuzvimirira?
    • Nekuronga mapfundo nemaarufabheti
    • Nekushandura mapoinzi kuita mapixel kutanga
    • Nokurasa zvose asi pfungwa yokutanga
    • Nekushandisa symmetric function senge max-pooling pamusoro pe-per-point features
  5. Chii chinoitwa neIterative Closest Point (ICP) algorithm?
    • Anoenzanisa makore maviri ane mapoinzi muchimiro chakafanana chekubatanidza
    • Inodzvanya makore ekuchengetera
    • Mavara anonongedzera nekukwirira
    • Inoona zviso mu 3D scans
  6. Sei voxel downsampling ichinyanya kushandiswa?
    • Kuti uwedzere mamwe mapoinzi kune ruzivo
    • Kuderedza makore makuru, akasvibira kusvika pakugoneka, saizi yakafanana
    • Kushandura 3D data kuita odhiyo
    • Kuti encrypt iyo point Cloud
  7. What does PointNet++ add beyond PointNet’s independent per-point feature processing?
    • A fixed left-to-right order for every point cloud
    • An image-only convolution applied to RGB pixels
    • Hierarchical grouping of nearby points to learn local features at multiple scales
    • A rule that deletes every point outside the origin
  8. Why might a point-cloud pipeline remove statistical outliers before modeling?
    • To create an ordered sequence from the remaining points
    • To convert the cloud into a two-dimensional audio spectrogram
    • To add synthetic surface normals to every point
    • To discard isolated noisy points that do not fit the surrounding point distribution