Systems and Methods for Predicting Instance Geometry

1 . A computer-implemented method, the method comprising:

obtaining, by a computing system comprising one or more computing devices, an input image comprising a plurality of datapoints indicative of an environment;

determining, by the computing system, an instance mask for an object instance within the environment by inputting the input image to a machine-learned instance segmentation model;

determining, by the computing system, one or more initial polygons for the object instance based, at least in part, on the instance mask, wherein the initial polygon comprises a plurality of initial vertices defining one or more initial edges of the one or more initial polygons;

obtaining, by the computing system, a feature embedding comprising one or more features for one or more datapoints associated with the object instance;

determining, by the computing system, a vertex embedding based, at least in part, on the feature embedding and the one or more initial polygons, wherein the vertex embedding is indicative of the locations of one or more of the initial vertices of the one or more initial polygons; and

generating, by the computing system, one or more enhanced polygons for the object instance based, at least in part, on the vertex embedding and the one or more initial polygons, wherein the one or more enhanced polygons comprise a plurality of enhanced vertices defining one or more enhanced edges of the one or more enhanced polygons.

2 . The computer-implemented method of claim 1 , wherein generating the one or more enhanced polygons for the object instance based, at least in part, on the vertex embedding and the initial polygon comprises:

determining, by the computing system, a plurality of vertex offsets by inputting the vertex embedding to a machine-learned deforming model; and

determining, by the computing system, the plurality of enhanced vertices of the one or more enhanced polygons by applying the plurality of vertex offsets to the plurality of initial vertices of the one or more initial polygons.

3 . The computer-implemented method of claim 2 , wherein the plurality of vertex offsets comprise a vertex offset for each initial vertex of the plurality of initial vertices,

wherein a respective initial vertex of the plurality of initial vertices is indicative of initial coordinates of the input image, and

wherein a respective vertex offset of the plurality of vertex offsets comprises a distance from respective initial coordinates of a corresponding initial vertex.

4 . The computer-implemented method of claim 3 , wherein determining the plurality of enhanced vertices of the one or more enhanced polygons by applying the plurality of vertex offsets to the plurality of initial vertices comprises:

adding, by the computing system, the distance of the respective vertex offset to the respective initial coordinates of the corresponding initial vertex.

5 . The computer-implemented method of claim 1 , wherein generating the one or more enhanced polygons for the object instance based, at least in part, on the vertex embedding and the one or more initial polygons comprises:

modeling, by the computing system, dependencies among the plurality of initial vertices.

6 . The computer-implemented method of claim 1 , further comprising:

determining, by the computing system, an object image from the input image, wherein the object image comprises one or more datapoints of the plurality of datapoints; and

obtaining, by the computing system, the feature embedding comprising the one or more features for the one or more datapoints of the plurality of datapoints by inputting the object image to a machine-learned feature extraction model.

7 . The computer-implemented method of claim 6 , wherein determining the object image from the input image comprises:

cropping, by the computing system, the input image based, at least in part, on the instance mask.

8 . The computer-implemented method of claim 7 , wherein cropping the input image based, at least in part, on the instance mask comprises:

fitting, by the computing system, a bounding box to the instance mask; and

cropping, by the computing system, the input image based at least in part on the bounding box.

9 . The computer-implemented method of claim 5 , wherein the feature embedding comprises a feature tensor for each of the one or more datapoints of the object image.

10 . The computer-implemented method of claim 1 , wherein the plurality of datapoints comprise a plurality of image pixels of the input image; and

wherein the instance mask comprises a coarse pixel-wise segmentation mask of the object instance.

11 . The computer-implemented method of claim 10 , wherein determining the one or more initial polygons for the object instance based, at least in part, on the instance mask comprises:

applying, by the computing system, a contour algorithm to extract one or more object contours from the instance mask; and

determining, by the computing system, the plurality of initial vertices for the one or more initial polygons based, at least in part, on the one or more object contours.

12 . The computer-implemented method of claim 11 , wherein the plurality of initial vertices is placed an equal image pixel distance apart along the one or more object contours.

13 . The computer-implemented method of claim 1 , wherein the computing system is on board an autonomous vehicle, and wherein the method further comprises:

identifying, by the computing system, the object associated with the object instance based, at least in part, on the one or more enhanced polygons.

14 . A computing system comprising:

one or more processors; and

one or more non-transitory computer-readable media that collectively store instructions that, when executed by the one or more processors, cause the system to perform operations, the operations comprising:

obtaining an instance mask for an object instance within an environment depicted by an input image comprising a plurality of datapoints;

determining one or more initial polygons for the object instance based, at least in part, on the instance mask, wherein the one or more initial polygons comprise a plurality of initial vertices defining one or more initial edges of the one or more initial polygons;

obtaining a feature embedding comprising one or more features for one or more datapoints associated with the object instance;

determining a vertex embedding based, at least in part, on the feature embedding and the one or more initial polygons, wherein the vertex embedding is indicative of the locations of one or more of the initial vertices of the one or more initial polygons;

generating a plurality of vertex offsets by inputting the vertex embedding to a machine-learned deforming model; and

generating one or more enhanced polygons based, at least in part, on the plurality of vertex offsets and the plurality of initial vertices, wherein the one or more enhanced polygons comprise a plurality of enhanced vertices defining one or more enhanced edges of the one or more enhanced polygons.

15 . The computing system of claim 14 , wherein obtaining the instance mask for the object instance within the environment depicted by the input image comprises:

obtaining the input image comprising the plurality of datapoints indicative of the environment; and

determining, by the computing system, an instance mask for the object instance within the environment by inputting the input image to a machine-learned instance segmentation model.

16 . The computing system of claim 14 , wherein the operations further comprise:

obtaining a ground truth polygon corresponding to the object instance;

determining a ground truth loss for the machine-learned deforming model based, at least in part, on a comparison between the ground truth polygon and the one or more enhanced polygons; and

training the machine-learned deforming model to minimize the ground truth loss.

17 . The computing system of claim 14 , wherein the operations further comprise:

obtaining a standard deviation loss for the one or more enhanced polygons, wherein the standard deviation loss is indicative of an average displacement of a distance between each of the enhanced vertices; and

training the machine-learned deforming model to minimize the standard deviation loss.

18 . A computing system, comprising:

an image database comprising a plurality of input images, wherein each respective input image comprises a plurality of respective datapoints indicative of an environment;

a machine-learned instance segmentation model configured to output one or more object instances in response to receiving a respective input image of the plurality of input images;

a memory that stores a set of instructions; and

one or more processors which are configured to use the set of instructions to:

obtain an input image from the image database;

determine an instance mask for an object instance within an environment of the input image by inputting the input image to the machine-learned instance segmentation model;

determine one or more initial polygons for the object instance based, at least in part, on the instance mask, wherein the one or more initial polygons comprise a plurality of initial vertices defining one or more initial edges of the one or more initial polygons;

obtain a feature embedding comprising one or more features for one or more datapoints associated with the object instance;

determine a vertex embedding based, at least in part, on the feature embedding and the one or more initial polygons, wherein the vertex embedding is indicative of the locations of one or more of the initial vertices of the one or more initial polygons; and

generate one or more enhanced polygons for the object instance based, at least in part, on the vertex embedding and the one or more initial polygons, wherein the one or more enhanced polygons comprise a plurality of enhanced vertices defining one or more enhanced edges of the one or more enhanced polygons.

19 . The computing system of claim 18 , further comprising:

a machine-learned feature extraction model configured to output one or more features for one or more respective datapoints in response to receiving the one or more respective datapoints;

wherein the one or more processors are configured to use the set of instructions to:

obtain an object image from the input image, wherein the object image comprises one or more datapoints of the plurality of datapoints; and

determine the feature embedding by inputting the object image to the machine-learned feature extraction model.

20 . The computing system of claim 18 , further comprising:

a machine-learned deforming model configured to output a plurality of respective vertex offsets in response to receiving a respective vertex embedding;

wherein the one or more processors are configured to use the set of instructions to:

determine a plurality of vertex offsets by inputting the vertex embedding to the machine-learned deforming model; and

determine the plurality of enhanced vertices of the one or more enhanced polygons by applying the plurality of vertex offsets to the plurality of initial vertices.