Merge pull request #1 from Esri/jy-rvw-3D-guide

Update case of link text for proper navigation

Author: vbhv14

guide/14-deep-learning/point_cloud_classification_using_point_transformer.ipynb

@@ -59,9 +59,9 @@
    "source": [
     "When it comes to classifying point clouds, deep learning and neural networks are a great choice since they offer a scalable and efficient architecture. They have enormous potential to make manual or semi-assisted classification modes of point clouds a thing of the past. With that in mind, we can take a closer look at the Point Transformer V3 model included in `arcgis.learn` and how it can be used for point cloud classification.\n",
     "\n",
-    "Point Transformer V3 (PTv3) is a new and improved point transformer model that builds upon the successes of its predecessors, PTv1 and PTv2. It's designed with a focus on simplicity, efficiency, and performance. One of the key improvements in PTv3 over PTv1 is the introduction of grouped vector attention (GVA). This mechanism allows for efficient information exchange within the model, leading to better performance. PTv3 also boasts a receptive field that is 64 times wider than PTv1, enabling it to capture a broader context of the point cloud data. <a href=\"#References\">[1]</a> It replaces the computationally expensive KNN neighbor search with a more efficient serialized neighbor mapping. The complex attention patch interaction mechanisms of PTv2 are also simplified in PTv3, further enhancing efficiency. Moreover, PTv3 replaces relative positional encoding with a prepositive sparse convolutional layer, contributing to its overall simplicity and performance.\n",
+    "Point Transformer V3 (PTv3) is a new and improved point transformer model that builds upon the successes of its predecessors, PTv1 and PTv2. It's designed with a focus on simplicity, efficiency, and performance. One of the key improvements in PTv3 over PTv1 is the introduction of grouped vector attention (GVA). This mechanism allows for efficient information exchange within the model, leading to better performance. PTv3 also boasts a receptive field that is 64 times wider than PTv1, enabling it to capture a broader context of the point cloud data. <a href=\"#references\">[1]</a> It replaces the computationally expensive KNN neighbor search with a more efficient serialized neighbor mapping. The complex attention patch interaction mechanisms of PTv2 are also simplified in PTv3, further enhancing efficiency. Moreover, PTv3 replaces relative positional encoding with a prepositive sparse convolutional layer, contributing to its overall simplicity and performance.\n",
     "\n",
-    "It's worth noting that PTv3's strength also lies in its ability to effectively capture local context and structural information within point clouds. This is crucial for various 3D understanding tasks such as classification, segmentation, and object detection. <a href=\"#References\">[1]</a>\n"
+    "It's worth noting that PTv3's strength also lies in its ability to effectively capture local context and structural information within point clouds. This is crucial for various 3D understanding tasks such as classification, segmentation, and object detection. <a href=\"#references\">[1]</a>\n"
    ]
   },
   {
@@ -101,7 +101,7 @@
     "</center>\n",
     "</p>\n",
     "<br>\n",
-    "<center>Figure 2. Four point cloud serialization patterns are shown, each with a triplet visualization. The triplets show the serialization curve, sorting order, and grouped patches for local attention. <a href=\"#References\">[1]</a>. </center>"
+    "<center>Figure 2. Four point cloud serialization patterns are shown, each with a triplet visualization. The triplets show the serialization curve, sorting order, and grouped patches for local attention. <a href=\"#references\">[1]</a>. </center>"
    ]
   },
   {
@@ -132,7 +132,7 @@
     "</center>\n",
     "</p>\n",
     "<br>\n",
-    "<center>Figure 3. Visualizing Patch Interaction Methods: (a) Standard Patch Grouping: Depicts a regular, non-shifted patch arrangement. (b) Shift Dilation: Shows a dilated effect achieved by grouping points at regular intervals. (c) Shift Patch: Illustrates a shifting mechanism akin to a shifting window. (d) Shift Order: Represents cyclically assigned serialization patterns to successive attention layers. (e) Shuffle Order: Demonstrates randomized serialization pattern sequences fed to attention layers.  <a href=\"#References\">[1]</a>. </center>"
+    "<center>Figure 3. Visualizing Patch Interaction Methods: (a) Standard Patch Grouping: Depicts a regular, non-shifted patch arrangement. (b) Shift Dilation: Shows a dilated effect achieved by grouping points at regular intervals. (c) Shift Patch: Illustrates a shifting mechanism akin to a shifting window. (d) Shift Order: Represents cyclically assigned serialization patterns to successive attention layers. (e) Shuffle Order: Demonstrates randomized serialization pattern sequences fed to attention layers.  <a href=\"#references\">[1]</a>. </center>"
    ]
   },
   {
@@ -415,9 +415,9 @@
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@@ -429,7 +429,7 @@
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guide/14-deep-learning/point_cloud_object_detection_using_second.ipynb

@@ -26,7 +26,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The `arcgis.learn` module supports point cloud object detection as a downstream task using the Sparsely Embedded Convolutional Detection (SECOND) architecture <a href=\"#References\">[1]</a>. This architecture enables the detection of 3D objects within a point cloud dataset, represented by 3D bounding boxes (multipatch feature class). Point cloud datasets are typically acquired using LiDAR sensors, which employ laser beams to sample the Earth's surface and generate precise x, y, and z coordinates. These points, collectively referred to as \"point clouds,\" are commonly generated through terrestrial and airborne LiDAR surveys.\n",
+    "The `arcgis.learn` module supports point cloud object detection as a downstream task using the Sparsely Embedded Convolutional Detection (SECOND) architecture <a href=\"#references\">[1]</a>. This architecture enables the detection of 3D objects within a point cloud dataset, represented by 3D bounding boxes (multipatch feature class). Point cloud datasets are typically acquired using LiDAR sensors, which employ laser beams to sample the Earth's surface and generate precise x, y, and z coordinates. These points, collectively referred to as \"point clouds,\" are commonly generated through terrestrial and airborne LiDAR surveys.\n",
     "\n",
     "Point clouds consist of collections of 3D points that contain location information (x, y, and z coordinates) along with additional attributes such as GPS timestamps, intensity, and number of returns. Intensity reflects the strength of the returning laser pulse, while the number of returns indicates how many times a given pulse was reflected. LiDAR data can be combined with RGB (red, green, and blue) bands derived from imagery captured concurrently with the LiDAR survey.\n",
     "\n",
@@ -57,7 +57,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "With that in mind, we can take a closer look at the Sparsely Embedded Convolutional Detection (SECOND) model included in `arcgis.learn` and how it can be used for point cloud object detection.<a href=\"#References\">[1]</a>\n"
+    "With that in mind, we can take a closer look at the Sparsely Embedded Convolutional Detection (SECOND) model included in `arcgis.learn` and how it can be used for point cloud object detection.<a href=\"#references\">[1]</a>\n"
    ]
   },
   {
@@ -343,9 +343,9 @@
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@@ -357,7 +357,7 @@
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