Learning 3D Dense Correspondence via Canonical Point Autoencoder

1 Introduction . With prior knowledge and experience , humans can easily perceive corresponding object parts ( e.g. , the wings from two different airplanes ) , understand their shape and appearance variance , in order to distinguish different objects coming from the same category . In computer vision , modeling dense correspondence between 3D shapes in one category is fundamental for numerous applications , such as robot grasping [ 1 , 2 ] , object manipulation [ 3 ] and texture mapping [ 2 , 4 ] . However , existing 3D cameras typically capture raw point clouds of shape surfaces that are arbitrarily-ordered and unstructured , in which correspondences are not established . 3D mesh representation , although is usually parameterized with UV maps that can indicate correspondences , can not be directly obtained from sensors and needs to be reconstructed from other types of representations , e.g. , 2D images [ 5 , 6 ] or 3D point clouds [ 7 ] . In this work , we focus on learning point cloud correspondences , which remains an open challenge since it is infeasible to label ground truth correspondence annotations . Without ground truth annotations , existing methods mainly discover shape correspondences via seeking a form of canonical space that can associate various instance shapes . For example , in particular shape domains such as human bodies [ 8 ] and human faces [ 9 ] , parameterized shape primitives have been designed to fit the observed raw data and to obtain the correspondences . Such designs , however , can not be generalized to other categories , e.g. , man-made objects [ 10 ] . Recently , several part co-segmentation networks relax the requirements of specific parameterized primitives , but instead decompose input shapes into an ordered group of simplest part constitutions [ 11 , 12 ] , in a self-supervised manner . These methods , however , require careful selection of the autoencoder architectures ( i.e. , they need to be considerably shallow to let the branches only able to represent simple shapes ) , and the number of part bases . Moreover , such part-based representation does not explicitly provide fine-grained ( e.g. , point-level ) correspondences . 35th Conference on Neural Information Processing Systems ( NeurIPS 2021 ) . In this work , we introduce a novel canonical space where dense ( i.e. , point-level ) correspondences for all the shapes of a category can be explicitly obtained from . Inspired by 3D mesh representation [ 5 , 13 , 14 , 15 ] where shapes from one category are represented as deformations on top of a shape primitive , in our work , we set the canonical space as a 3D UV sphere . Our goal is to learn a “ point cloud-to-sphere mapping ” such that corresponding parts from different instances overlap when mapped onto the canonical sphere . In other words , similar to the mesh representation , a unique UV coordinate can represent the same semantic point/local region of shapes ( e.g. , the tip of an aeroplane ’ s wing ) , regardless of shape variations . Towards this goal , we introduce the canonical point autoencoder ( CPAE ) : we place the sphere primitive at the bottleneck ; the encoder non-linearly maps each individual input shape to the sphere primitive , where the decoder deforms the primitive back to match the original shape . We show that with several self-supervised objectives , this autoencoder architecture effectively ( 1 ) enforces the input points warped to the surface of the sphere primitives , and ( 2 ) encourages those corresponding points from different instances mapped to the same location on the sphere – both guarantee that the network learns correct dense correspondences . Essentially , we do not assume all object shapes in one category having the same topology , e.g. , an armchair does not have correspondence on its armrests , with another instance without an armrest . To introduce such uncertainty for correspondence matching , we propose an adaptive Chamfer loss on the bottleneck to allow customized primitive for each instance . As such , we are able to determine if a point on one instance has a correspondence in another point cloud . One advantage of the proposed method compared to the recent work [ 12 ] is that we can learn correspondences even when instances in the training dataset are not aligned , i.e. , our model is rotationinvariant within a certain rotation range and does not need to predict an additional rotation matrix as used in [ 12 ] . The main contributions of this work are : • We introduce a novel canonical space – a UV sphere , that explicitly represents dense correspondences of shapes from one category . • With the canonical space on the bottleneck , we design an autoencoder that learns such a “ point cloud-to-sphere mapping ” via a group of self-supervised objectives . • We apply the proposed method on various categories and quantitatively evaluate on the task of 3D semantic keypoint transfer and part segmentation label transfer , achieving comparable if not better performance than state-of-the-art methods . 2 Related Work . Deep Learning on Point Clouds . As a flexible and memory efficient representation of 3D shapes , point cloud has been widely studied and combined with deep neural networks . The PointNet [ 16 ] solves point cloud classification and segmentation by using MLPs and a max pooling layer to aggregate 3D shape information . One crucial property of the PointNet is that it is able to handle unordered input and is thus invariant to point permutations . In our work , we utilize the first few layers of the PointNet as our shape encoder . Another line of works [ 17 , 18 , 19 , 20 ] focus on reconstructing or generating point clouds . The most representative and related to our work is the FoldingNet [ 17 ] , where a point cloud is first encoded by a graph-based encoder and then reconstructed by sequentially applying the “ folding operation ” ( instantiated by MLPs ) to a 2D UV map . In this work , we reveal why the MLPs are able to preserve point order and utilize it as building blocks in our CPAE . Primitive Fitting . Primitive fitting has a long history in computer vision [ 21 ] . Several approaches focus on fitting individual samples with shape priors , such as blocks world [ 21 ] , generalized cylinders [ 22 ] , geons [ 23 ] , Lego pieces [ 24 ] , qualitative 3D blocks [ 25 ] , and patches [ 26 ] . However , these primitives are analyzed per-instance without order , therefore , can not be used for the correspondence . Some other approaches focus on fitting over a collection of shapes , which provide ordered set of primitives [ 11 , 27 , 28 , 29 , 30 , 31 , 32 ] . However , most of these approaches can only infer part-level correspondences and require additional supervision such as the number of primitives . In this work , we directly learns set-to-set alignment between point clouds , thus requires no shape prior . 3D Dense Correspondence . Given a pair of source and target instance in the same category , 3D dense correspondence learning targets at finding a corresponding point in the target instance for each point from the source instance . Several approaches [ 33 , 34 , 35 ] resolve this task through point cloud registration , using labeled pairwise correspondence as supervision . To relax constraints on supervision , Bhatnagar et al . [ 36 ] predict part correspondences to a template via implicit functions . Unfortunately , they require part labels for training . To unsupervisedly learn 3D dense correspondence , Chen et al . [ 37 ] propose a method to learn 3D structure points that are consistent across different instances . However , the model assumes structure similarity among different instances , which ignores intra-class variants and fails to detect non-existing correspondences between dissimilar shapes in the same category . Most relevant to our work , Liu et al . [ 12 ] introduce an unsupervised approach that leverages part features learned by the BAE-NET [ 11 ] to build dense correspondences . Their algorithm is able to calculate a confidence score representing the probability of correspondence . However , in order to train the implicit function , they require additional knowledge of object surface to compute the occupancy . Moreover , the correspondence learning in [ 12 ] heavily relies on the completeness of the part features from the BAE-NET [ 11 ] , which can lead to incorrect correspondences for parts that BAE-NET can not separate ( e.g. , flat surfaces , objects with fine-grained details ) . In contrast , our approach directly learns dense correspondences from the point cloud with self-supervision . Cycle Consistency . Our cross reconstruction loss is related to literature that uses cycle consistency as a supervisory signal to learn correspondence without ground truth annotations . Some of these works exploit the consistency among different modalities ( e.g. , 2D-3D ) . Specifically , [ 38 ] aim to learn cross-instance pairwise correspondence for 2D images using cycle-consistency terms guided by 3D synthetic data . [ 39 ] and [ 40 ] both leverage cycle-consistency terms to enforce correspondence between a 2D image and a 3D canonical template . Aside from these works that focus on crossmodality , work from [ 41 ] is mostly related to ours . They propose a “ point-wise ” cycle consistency loss that explicitly enforces a point deformed through any cycle of deformations to be mapped back to the origin location . Different from their approach , our consistency is instance-wise and we do not assume bijection mapping among shapes . 3D Deformable Mesh Representations . Our algorithm is also related to methods [ 5 , 13 , 14 , 15 ] that represent instance shapes as mesh deformations of a mesh primitive , i.e . a sphere template . Vertices on each instance surface that are mapped to the same locations on the shape primitive are discovered as correspondences . One obvious limitation is that only genus 0 shapes , e.g. , birds [ 5 , 13 ] can be deformed from a mesh sphere . In contrast , ours does not have such restriction thanks to the proposed non-linear mappings , instead of explicit deformation . 3 Proposed Method . In this section , we introduce our end-to-end CPAE for learning dense correspondences from point clouds without ground truth annotation . Given a point cloud SA with individual point p ∈ R3 ( see Figure 1 ) , our model : ( 1 ) predicts its canonical primitive UA ( a “ deformed ” point cloud with the same number of points as SA ) , which is supposed to be as close as possible to the canonical sphere in the bottleneck ; ( 2 ) reconstructs the original input point cloud back from UA . We show that while the first ensures each input point cloud to be warped to the surface of the sphere primitive , the second indirectly encourages the corresponding points from different point cloud instances to overlap during mapping to the primitive . In the following , we first describe our network architecture , i.e. , the encoder and decoder modules . We then introduce the adaptive Chamfer loss and the reconstruction losses that are applied to each individual module . Finally , we show that our decoder reconstructs ordered point clouds , i.e. , different point cloud instances can fetch their correspondences directly via the point indices , which provides a more accurate inference . 3.1 Network Architecture . Our method learns two mapping functions : one maps each individual 3D point p ∈ R3 in the world coordinates to the canonical space Φ ( p ) = u , u ∈ R3 , while the other conducts the inverse mapping Φ−1 ( u ) = p. Instead of using a reversible network , we instantiate Φ ( · ) and Φ−1 ( · ) with two MLPs respectively , and cascade them to construct an autoencoder ( i.e. , Φ ( · ) is the encoder and Ψ ( · ) ≈ Φ−1 ( · ) is the decoder ) . However , this mapping function can not be generalized to different point clouds if it only receives the coordinate of a single point p as the input . Therefore , we formulate them as conditional mapping functions by concatenating the input with a shape latent code ZA that represents a unique input shape SA , e.g. , Φ ( p , ZA ) , in order to generalize the mapping functions to all instances in the same category . There are three key modules in the proposed method : ( 1 ) a PointNet encoder that produces the shape latent code ; ( 2 ) an encoder MLP ( denoted as canonical mapping ) that maps a point cloud to a primitive sphere , and ( 3 ) a decoder MLP ( denoted as inverse mapping ) that deforms the primitive sphere back to the input point cloud . PointNet Encoder . As shown in Figure 1 , given a point cloud SA ∈ Rk×3 with k points , we encode it using a PointNet [ 16 ] encoder in a way similar to [ 8 , 18 , 19 ] . Each 3D point of the input point cloud is represented as a 512 dimensional vector using an MLP with 3 hidden layers of 64 , 128 , 512 neurons and ReLU activations . We then aggregate all point features with max-pooling and a linear layer to generate a global latent code zA ∈ R512 . We use PointNet because it produces a robust latent code representing the global shape , which is also invariant to input permutation . Canonical Mapping Φ ( · ) . To learn the canonical mapping function , we concatenate each point with the global latent code [ p , Z ] ∈ R515 as input to the MLP , which then outputs a 3D coordinate u ∈ R3 in the canonical space . We construct a sphere point cloud by uniformly sampling a large number of points from a standard sphere mesh , and place them as the canonical primitive at the bottleneck . A Chamfer loss is used to measure the difference between the outputted point u and primitive to encourage the mapped point adhering to the surface ( see Eq . ( 1 ) ) . Ideally , all the mapped instances from the same category should be as close as possible to the canonical sphere primitive . However , an instance may include parts/regions that do not exist in other instances due to intra-class variation , e.g. , not all chairs include armrests . With a conventional Chamfer loss , the shapes for objects with rare components do not converge during training , i.e. , those rare components are mapped to locations that are far away from the primitive surface . Thus , we relax the bidirectional constraint of the Chamfer loss and allow each instance to produce its own “ instance primitive ” to some extent ( see UA in Figure 1 ) . This is formulated via an adaptive Chamfer loss LACD : LACD ( UA , U ) = 1 |UA| ∑ p∈UA min q∈U ∥p− q∥2 + α 1 |U | ∑ q∈U min p∈UA ∥q − p∥2 ( 1 ) where α ∼ [ 0 , 1 ] is an adaptive parameter indicating to what extent the predicted instance primitive should match a canonical sphere , UA and U are the instance primitive and the canonical UV primitive ( e.g. , a 3D UV sphere ) , respectively . When α = 1 , LACD is equivalent to calculating the conventional Chamfer distance between the unfolded primitive and the canonical UV primitive . During training , α is initialized to 1 and gradually decreased to 0 . This allows the canonical mapping to predict instance-aware primitive since the second term of the Chamfer loss no longer constraints the primitive to be consistent with a canonical sphere . In the experiments ( see Section 4.5 ) , we show that this design allows us to infer rare object components that have no correspondences in other instances since non-corresponding regions usually occupy a distinct area in the canonical space . Inverse Mapping Ψ ( · ) . Similarly , we utilize another MLP in the decoder , which receives the concatenation of one point from the bottleneck and the global shape code , i.e. , [ u , Z ] ∈ R515 , as the input . The function learns to map it back to its original world coordinate p , which can be fulfilled via a point-to-point reconstruction loss . We leverage an MSE loss LMSE , a Chamfer distance LCD and an Earth Mover Distance ( EMD ) LEMD between the reconstruction Ŝ and input point cloud P : Lrec ( P , Ŝ ) = µ1LMSE ( P , Ŝ ) + µ2LCD ( P , Ŝ ) + µ3LEMD ( P , Ŝ ) ( 2 ) where µ1 , µ2 , µ3 are the weights , and empirically determined , µ1 = 1e3 , µ2 = 10 , and µ3 = 1 . The inverse mapping bears some resemblance to the principle of FoldingNet [ 17 ] , which adopts an MLP architecture to map one 2D coordinate together with a global shape representation to the 3D world1 . However , since FoldingNet does not have the forward mapping function as ours and the input points are untraceable , it needs to use Chamfer loss , instead of a point-wise loss ( Eq . ( 2 ) ) as ours . Align Canonical Mappings via CrossReconstruction . With the encoder and objective function , the canonical mapping is able to map the input points to the surface of the canonical primitive . However , even with the reconstruction loss ( Eq . ( 2 ) ) , there is no guarantee that the corresponding points from different shape instances can be overlapped on the sphere – which is the key to learn dense correspondences . For instance , in Figure 1 , while SA is mapped to the frontal half of the sphere , it is likely that another shape SB will be mapped to the rear half . To better align the mapped points , we introduce a cross-structured decoder that leverages : ( 1 ) a self-reconstruction branch as presented in Eq . ( 2 ) , and ( 2 ) a cross-reconstruction branch . As shown in Figure 2 , we feed the combination of the predicted instance primitive UA of point cloud SA , with the shape latent vector ZB of another randomly sampled point cloud SB to the folding decoder . We then minimize the Chamfer distance between the output shape ŜA→B and SB . The cross-reconstruction loss between the point cloud SA and SB is : Lcross ( SA , SB ) = LCD ( ŜA→B , SB ) ( 3 ) The cross-reconstruction branch ensures even by swapping the predicted primitives UA and UB , the inverse mapping can still reconstruct their own shapes , conditioned on their own shape latent codes . That is , the decoder encourages UA and UB to overlap on the canonical primitive , and thus ensures the network learns correct correspondences . Relation to Implicit Function . We note that since both mapping functions Φ ( · ) and Ψ ( · ) process each input point independently , they can be interpreted as conditional implicit functions , which are widely applied for 3D shape reconstruction [ 42 , 43 ] , view synthesis [ 44 , 45 ] , and recently for self-supervised 3D correspondence [ 12 ] . Such an interpretation reveals that the MLPs indeed learn continuous mappings that are feasible for interpolations , e.g. , once the decoder MLP is learned , it is able to map any point on a continues sphere surface that is not necessarily among the mapped input u , to the world coordinate . E.g , one can sample points densely from the surface of the sphere to reconstruct a continuous surface or a 3D mesh .

Summary: The paper proposes a canonical point autoencoder for the purpose of unsupervised learning dense correspondences between 3D shapes of the same category. The canonical space is defined on a 3D sphere, but with extra flexibility that the template needs not to fully cover the full sphere. This gives the flexibility to generalize to non genus 0 shapes. The proposed method seems to give noticeable improvement over existing ones.