3D Shape Modeling for Few-Shot Depth Instance Segmentation
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Solution Overview
Problem
Existing instance segmentation methods require large training sets and significant annotation efforts, making them infeasible in many real-world scenarios, and unsupervised approaches need large unlabelled datasets or make assumptions on object shapes.
Innovation Solution
A method for instance segmentation in depth images using a few annotated examples, which involves training a neural network to classify surface geodesics between depth image points, discretizing these geodesics, and using ground truth annotations to learn implicit 3D object models, with initial segmentation enhanced by the Watershed Algorithm.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If standard deep learning solutions (Mask-RCNN) are used for instance segmentation, then segmentation accuracy can be achieved, but large training sets with significant annotation efforts are required
Solution Approach 1:
The patent segments the instance segmentation problem into two distinct components: (1) learning implicit 3D object models from few-shot annotated data, and (2) performing actual instance segmentation using these models. This is achieved by training a neural network to classify depth surface geodesics during the learning phase, then using the learned models to guide segmentation in the inference phase. The segmentation process itself is further divided into computing geodesics, classifying them via the trained network, and aggregating results into final segmentations.
Solution Approach 2:
The patent performs preliminary action by learning implicit 3D object models from a small set of annotated images before the actual segmentation task. The neural network is pre-trained to classify geodesics and capture object shape characteristics, creating reusable 3D models that can be applied to segment multiple instances in new images without requiring re-annotation. This preliminary modeling step enables the system to generalize from few-shot data to unseen instances.
2Ease of operation
If clustering algorithms (K-Means, Gaussian mixture modeling) are applied on depth image point clouds, then segmentation can be performed, but assumptions on point cloud distributions are made which reduces flexibility for varied object shapes
Solution Approach 1:
The patent changes the fundamental parameters used for segmentation from distribution-based features (mean, covariance in K-Means) to geodesic-based features that directly represent object surface geometry. By computing and classifying depth surface geodesics, the system adapts to arbitrary object shapes without making distributional assumptions. The neural network learns shape characteristics in terms of geodesic properties (length, curvature, depth variations) that are inherently adaptable to any convex object geometry.
Solution Approach 2:
The patent substitutes the mechanical clustering process (iterative point assignment based on distance to cluster centroids) with a learning-based approach using neural networks. Instead of forcing points into pre-defined cluster distributions, the system uses a trained neural network to classify geodesics and infer instance boundaries, replacing rigid mechanical clustering with flexible learned decision boundaries that adapt to object shapes.
3Quantity of substance
If InSeGAN is used for unsupervised instance segmentation, then large unlabelled training sets can be utilized, but large datasets with diverse instance arrangements are required which are difficult to obtain
Solution Approach 1:
The patent inverts the conventional approach by using supervised learning with a small annotated dataset to learn 3D object models, rather than using unsupervised learning with large unannotated datasets. Instead of letting the model discover object structures from abundant unlabelled data, the system leverages the rich geometric information in a small set of annotated images to learn precise 3D models, then applies these models to segment unannotated images. This inversion allows high-quality segmentation with minimal annotation effort.
Data Source
AI summary
An imaging controller is provided for segmenting instances from depth images including objects to be manipulated by a robot. The imaging controller includes an input interface configured to receive a depth image that includes objects, a memory configured to store instructions and a neural network trained to segment instances from the objects in the depth image, and a processor, coupled with the memory, configured to perform the instructions to segment a pickable instance using the trained neural network. The instructions include steps of selecting a tallest point in the depth image, defining a region using a shape such that the region surrounds the tallest point, sampling points in the region of the depth image, computing depth-geodesics between the tallest point and the sampled points, submitting the depth-geodesics to the neural network to segment the pickable instance among instances of the objects in the depth image, and an output interface configured to output a geometrical feature of the pickable instance to a manipulator controller of the robot.