This year, few industries have gained greater traction than logistics. With millions of people hunkered down at home, there is an enormous demand for food and other necessities. People all throughout the country are realising the importance of saying #ThankATrucker. We believe that automation has the potential to make trucks safer, truckers more valuable, and trucking more productive at Ike. We also believe that trucks provide a clear road to launching safe, dependable, and economically successful autonomous vehicles, which have been a long-term promise. We may limit the extent of the problem and minimise difficulties like passenger comfort by focusing on freight and highway travel. Even with this focus, there are still a lot of intriguing technological difficulties to be solved. Long-range object detection is one of the most challenging and important of these activities.
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The speed differential between truckers (mainly highway travel) and passenger cars (mostly city driving) is a significant one. To stop comfortably at 55 miles per hour, a vehicle must travel 150 metres, which means automated trucks must identify objects beyond this range. All of the self-driving object identification benchmarks (e.g., KITTI, NuScenes, Lyft, Waymo) use short-range (under 100 metre) LIDAR due to the industry's previous concentration on passenger cars. Long-range object detection, on the other hand, necessitates sensors with a long detection range (>200 metres), such as cameras, radar, and/or a growing collection of higher-power long-range LIDAR.
Furthermore, only a few articles have addressed how state-of-the-art (SOTA) approaches can be adapted to the challenges of long-range object detection. We evaluated the SOTA of 3D object detection prior to CVPR last year, focusing on the trade-offs between Bird's-Eye View (BEV) and Range View (RV) representations. We offer an updated assessment of the SOTA, and explore how these methods might be used to long-range 3D object identification for autonomous trucking, in light of the increased interest in trucking.
The Current State of 3D Object Detection
The field of computer vision is fast evolving; CVPR 2020 has a record-breaking 1,470 accepted papers and 127 workshop submissions. We now focus on the trade-offs between grid-based (e.g., voxelization) and grid-free (e.g., point sets and graphs) approaches for processing LIDAR point clouds, building on last year's blog article.
Methods based on grids
CNNs are powerful feature extractors that are at the heart of many machine learning systems that achieve superhuman performance on tasks ranging from gaming to object classification to cancer diagnosis. Despite this, CNNs need a fixed-dimensional grid input (e.g., a 224 x 224 x 3-channel RGB image). Autonomous vehicles, on the other hand, have traditionally depended largely on LIDAR, which enables precise object ranging but results in a sparse, uneven point cloud. As a result, a number of methods for converting LIDAR point clouds into the fixed-size grid required by a CNN backbone have been devised.
The voxelization of the point-cloud into a binary occupancy grid is an effective method (used in systems like PIXOR). However, within-voxel spatial information is lost as a result of this sampling technique. PointNet solves this problem by producing spatial encodings for each point, which are then aggregated locally within grid cells using a symmetrical operation like max() (Figure 1). Before re-encoding the grid-feature-augmented points back into grid cells, these aggregated characteristics are often distributed back to individual points, allowing the network to use non-local information. PointNet blocks have become more popular among top KITTI submissions, emphasising two recurring motifs in SOTA architectures: 1) learn features rather than hand-craft them, and 2) use non-local information.
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PointNet creates a spatial encoding for each point and aggregates the generated features into grid cells (see Figure 1). A simplified version of the PointNet blocks used in PointPillars is shown in the diagram above. (a) When a point is created, it merely has its spatial coordinates as a feature. (a) Cell-level characteristics are accumulated (in this case, the centroid of points in the cell and the cell center). (c)The offsets of these cell-wise features are added to the points. (d) A trainable neural network converts point-wise features into point-wise embeddings. (e) With a symmetric operation like max(), point-wise embeddings are pooled per-cell.
These ideas have recently been applied to the problem of choosing the input grid resolution for CNNs, which includes a trade-off between performance and speed. A finer grid collects granular geometry features and improves item localization, but it takes longer to infer. As a result, multiple articles have proposed multi-scale feature fusion algorithms that combine features across grid resolutions (similar to Feature Pyramid Networks or Deep Layer Aggregation for CNN feature extraction). Instead of employing a single grid resolution, the network learns to incorporate data from many scales.
The Hybrid Voxel Network (HVNet), for example, gives points to voxels of various sizes before scattering these multi-scale voxel-wise properties back into points using attention (Figure 2). PV-RCNN encodes CNN-generated features in keypoints with multiple receptive fields in a similar manner. HRNet even proposes a novel CNN backbone that keeps both high-resolution and low-resolution feature maps throughout the feature extraction process.
HVNet performs multi-scale feature fusion at many sites (see Figure 2). [1] is the source for this illustration.
The challenge of setting non-maximum suppression (NMS) for anchors has also taken on the theme of learning rather than hand-crafting parameters. Many SOTA object detectors use anchors as object bounding box priors. The overlapping anchors, which reflect duplicate detections of the same object, are then filtered using NMS. Hand-tuned parameters, such as anchor dimensions or an intersection-over-union (IoU) threshold, are required for both approaches. To avoid the requirement for parameter tuning, AFDet, which now sits atop the Waymo 3D Leaderboard, expands Objects as Points (Figure 3) by identifying object positions using an anchor-free heatmap and immediately regressing object attributes with five convolutional heads.
Figure 3: ObjectsAsPoints' heatmap-based object detection technology learns to regress object bounding boxes directly, rather than depending on hand-specified anchors and IoU thresholds (left) (right). [2] is the source for this illustration.
Methods that don't use a grid
Although the preceding methods reduce the loss of spatial information caused by compacting LIDAR point clouds into a grid, a rising number of grid-free solutions that preserve the point cloud's spatial structure overcome this issue. PointNet is frequently used in grid-free approaches to aggregate local point features into greater global features while maintaining point symmetry. Grid-free approaches, on the other hand, must still decide how to group points in order to compute global characteristics and store them for later use.
Many grid-free implementations leverage the set abstraction (SA) layers proposed in PointNet++ for this purpose (Figure 4). SA layers can be layered similarly to convolutional layers to store global information in a few adaptively sampled keypoints, which can subsequently be utilised as input to another SA layer. PointNet++ infers global structure from local regions, supports multi-scale feature fusion, and propagates global knowledge to local points in general.
PointNet++ established a framework for hierarchical grid-free local-to-global feature aggregation and global-to-local feature projection, as seen in Figure 4. [3] is the source for this illustration.
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F-Pointnet was a pioneering grid-free approach for 3D object identification, built on the PointNet++ backbone. It employs two stages (like Faster-RCNN) to provide object bounding box proposals, the first of which is a 2D image-based object detector, followed by a PointNet++-based stage to refine these bounding box estimates. PointRCNN leverages PointNet++ in the proposal generation stage, but otherwise follows the same two-stage model. Sparse-To-Dense (STD) lowers latency by voxelizing the features for each of the suggestions, providing a more compact representation that is efficiently employed by fully-connected (FC) layers in the second stage, based on the observation that SA layers are slower than a CNN backbone. Graph-based algorithms, such as PointGNN, obviate the requirement for recurrent SA layer sampling and grouping operations and show good KITTI performance, although they have not yet been optimised for latency.