Patent Description:
In practical applications, many 3D models consist of a large number of connected components. And these multi-connected 3D models usually contain lots of repetitive structures in various transformations, as shown in <FIG>. Efficient compression methods for this kind of 3D models should be able to extract the redundancy existing in the repetitive structures.

An efficient compression algorithm for multi-connected 3D models, by taking advantage of discovering repetitive structures in the input models, was proposed in "Efficient Compression Scheme for Large 3D Engineering Models," <CIT>, and assigned to Thomson Licensing.

It discovers the structures repeating in various positions, orientations and scaling factors. Then the 3D model is organized into a "pattern-instance" representation. A pattern is the representative geometry of the corresponding repetitive structure. The connected components belonging to a repetitive structure are called instances of the corresponding pattern and are represented by their transformation, i.e. the positions, orientations and possible scaling factors, regarding to the pattern. The orientation of an instance is represented by two orthogonal axes represented by (x0, y0, z0) and (x1, y1, z1) in a Cartesian coordinate system, or (alpha, beta, gamma) in a spherical coordinate system.

A compressed bitstream syntax and semantics is disclosed that relates to a repetitive structure discovery based compression algorithm, which has been proven to be more efficient than the static 3D model compression algorithms provided by MPEG-3DGC. The disclosed compressed bitstream syntax and semantics of our repetitive structure discovery based compression algorithm is applicable, for example, to MPEG.

The present invention is about the compressed bitstream syntax and semantics.

The present invention also provides a system and a method for encoding and decoding a bitstream for a 3D model having repetitive structures.

An apparatus that utilizes the method is disclosed below.

The present invention also provides a computer readable medium having executable instructions to cause a computer to perform a method comprising corresponding steps for encoding or decoding a bitstream for a 3D model having repetitive structures.

Three-dimensional (3D) meshes are widely used in various applications for representing 3D objects, such as video games, engineering design, e-commerce, virtual reality, and architectural and scientific visualization. Usually their raw representation requires a huge amount of data. However, most applications prefer compact 3D mesh representation for storage or transmission. Typically, 3D meshes are represented by three types of data: connectivity data, geometry data and property data. Connectivity data describe the adjacency relationship between vertices, geometry data specify vertex locations, and property data specify attributes such as the normal vector, material reflectance and texture coordinates. Most 3D compression algorithms compress connectivity data and geometry data separately. The coding order of geometry data is determined by the underlying connectivity coding. Geometry data is usually compressed by three main steps: quantization, prediction and statistical encoding. 3D mesh property data are usually compressed in a similar manner.

The present invention is related to an efficient compression method for large 3D engineering models. Such models are often composed of several partitions, so-called "connected components". The redundancy in the representation of repeating geometric feature patterns can be reduced by regarding all the connected components that are equivalent (e.g. after normalization of position, size) as instances of one geometry pattern. Equivalent components can be clustered. A cluster may refer to only some, or to all components of a 3D model. Then each connected component can be represented by an identifier, such as an alphanumeric identifier, of the corresponding geometry pattern (or clustering class) and the transformation information which can reconstruct the component from the geometry pattern. This transformation information may exemplarily comprise one or more of scale factors, mean (or center), orientation axes (or and rotation information, respectively), or shift information. In principle, also others are possible.

The encoded model can be represented, transmitted and/or stored as a bitstream.

While we want the bitstream to embed all the transformation data, we also want it to be efficient and to address several applications, where sometimes either bitstream size or decoding efficiency or error resilience matters the most.

Therefore, two options are disclosed for how to put the transformation data of one instance, i.e. its position, orientation and scaling factor, in the bitstream. Both of them have their own advantages. An adaptive combination of both is particularly advantageous.

Option (A) is called grouped instance transformation mode: Using this mode, the position, orientation and possible scaling factor of one instance are packed together in the bitstream.

Option (B) is called separate instance transformation mode: The positions, orientations or possible scaling factors of all instances are packed together in the bitstream. In other words, the position, orientation and possible scaling factor of one instance are packed separately in the bitstream.

A decoder that uses option (B) has also the following features.

Our bitstream definition includes both the above two options (A) and (B). Then the user, or an automatic control, can choose the one which fits their one or more applications better.

The general structure of the compressed bitstream of our repetitive structure discovery based compression algorithm, A3DMC, is as shown in <FIG>.

The bitstream starts with the header buffer (A3DMC_stream_header), which contains all the necessary information for decoding the compressed stream: whether there is any repetitive structure in the original model, the 3D model compression method used for compressing patterns and other parts if necessary, whether the "grouped instance transformation mode" or "separate instance transformation mode" is used in this bitstream, whether there are some parts of the original model which are not included in any repetitive structure (unique part), etc..

If there is no repetitive structure in the original model (repeat_struc_bit != <NUM>), the left part (e.g. the beginning) of the bitstream is the compressed input 3D model using the 3D model compression method indicated in A3DMC_stream_header. Otherwise, the next part in the bitstream is the compressed result of all patterns. Depending on which instance transformation packing mode is chosen in this bitstream, either compr_insta_grouped_data or compr_insta_separate_data is the next part in the bitstream. If there is unique part in the original 3D model, compr_uni_part_data is attached. Otherwise, the bitstream ends.

The compressed bitstream syntax and semantics of A3DMC, will be explained in details as follows.

Specification of syntax functions, categories, and descriptors.

A3DMC_stream_header: contains the header buffer. A3DMC_steam_data: contains the data buffer. A3DMC_stream_header class.

repeat_struc_bit : a <NUM>-bit unsigned integer indicating whether there are repetitive structures in the 3D model. <NUM> means no repetitive structure and <NUM> means repetitive structure. 3d_model_compr_mode : a <NUM>-bit unsigned integer indicating the 3D model compression method used to compress patterns, unique part and the original 3D model itself if it includes no repetitive structures.

QP: a <NUM>-bit unsigned integer indicating the quantization parameter. , the minimum value of QP is <NUM> and the maximum is <NUM>. pattern_num: a <NUM>-bit unsigned integer indicating the number of all patterns if it is less than <NUM>. The minimum value of pattern_num is <NUM>. pattern_num_2: a <NUM>-bit unsigned integer indicating the number of all patterns if it is not less than <NUM>. In this case, the total pattern number is (pattern_num_2 + <NUM>)
instance_num: a <NUM>-bit unsigned integer indicating the number of all instances if it is less than <NUM>. The minimum value of instance_num is <NUM>. instance_num_2: a <NUM>-bit unsigned integer indicating the number of all instances if it is not less than <NUM>. In this case, the total instance number is (instance_num_2 + <NUM>)
insta_trans_group_bit : a <NUM>-bit unsigned integer indicating whether "grouped instance transformation mode" or "separate instance transformation mode" is used in this bitstream. <NUM> for "separate instance transformation mode" and <NUM> for "grouped instance transformation mode". insta_orient_mode_bit : a <NUM>-bit unsigned integer indicating the encoding mode of instance orientation. <NUM> means spherical mode and <NUM> Cartesian mode. use_scaling_bit : a <NUM>-bit unsigned integer indicating whether instance transformation include scaling factors. <NUM> for scaling factors being included in instance transformation and <NUM> for not. uni_part_bit : a <NUM>-bit unsigned integer indicates whether there is unique part in the original 3D model. <NUM> means there is no unique part and <NUM> means there is unique part. reserved_bits : a <NUM>-bit unsigned integer which is always <NUM> and used for byte alignment.

compr_repeat_struc_data: contains the compressed 3d model, which includes repetitive structures. compr_3d_model_data: contains the compressed 3d model, which has no repetitive structures and is encoded by the compression method indicated by 3d_model_compr_mode. compr_repeat_struc_data class.

compr_pattern_data: contains the compressed pattern data of all patterns, which is encoded by the compression method indicated by 3d_model_compr_mode. compr_insta_grouped_data : contains the compressed instance transformation data using the "grouped instance transformation mode". compr_insta_separate_data: contains the compressed instance transformation data using the "separate instance transformation mode". compr_uni_part_data: contains the compressed unique part data, which is encoded by the compression method indicated by 3d_model_compr_mode.

The orientation of ith instance in Cartesian mode is represented by <NUM> orthogonal axes (x0, y0, z0) and (x1, y1, z1).

compr_ith_insta_orient_x0: contains the compressed x0 of ith instance's orientation. compr_ith_insta_orient_y0: contains the compressed y0 of ith instance's orientation. compr_ith_insta_orient_z0_sgn: a <NUM>-bit unsigned integer indicating the sign of z0 needed for calculating z0 using x0 and y0. <NUM> for "-" and <NUM> for "+". compr_ith_insta_orient_z0_res: contains the compressed residual of z0 which is calculated by (z0 - computer_z0()). compr_ith_insta_orient_z1: contains the compressed z1 of ith instance's orientation. ith_insta_orient_x1_sgn: a <NUM>-bit unsigned integer indicating the sign of x1 needed for calculating x1 using x0, y0. <NUM> for "-" and <NUM> for "+". ith_insta_orient_y1_sgn: a <NUM>-bit unsigned integer indicating the sign of y1 needed for calculating y1 using x0, y0. <NUM> for "-" and <NUM> for "+". compr_ith_insta_orient_x1: contains the compressed x1 of ith instance's orientation. compr_ith_insta_orient_y1: contains the compressed y1 of ith instance's orientation. ith_insta_orient_delta_sgn: a <NUM>-bit unsigned integer indicating the sign needed for calculating x1 or y1 using x0, y0, z0 and y1 or x1. <NUM> for "-" and <NUM> for "+". compr_ith_insta_orient_z1_res: contains the compressed residual of z1 which is calculated by (z1 - computer_z1())
threshold: a threshold widely accepted in compression field. compute_z0(): compute z0 of the ith instance using x0, y0 and z0 sign. bit_num_orient_cartesian(): compute the number of bits for each orientation value in cartesian coordinate system based on QP. bit_num_orient_res_cartesian() : compute the number of bits for each orientation residual value in cartesian coordinate system based on QP. compute_z1() : compute z1 of the ith instance using x0, y0, z0, x1 and y1. compr_ith_insta_orient_spherical class.

The orientation of ith instance in spherical mode is represented by <NUM> angles, alpha, beta & gamma.

compr_ith_insta_orient_alpha: contains the compressed alpha of ith instance's orientation. compr_ith_insta_orient_beta: contains the compressed beta of ith instance's orientation. compr_ith_insta_orient_gamma: contains the compressed gamma of ith instance's orientation. compr_ith_insta_orient_res : contains the compressed residual in Cartesian coordinate system of ith instance's orientation. bit_num_orient_alpha(): compute the number of bits for each alpha value based on QP
bit_num_orient_beta(): compute the number of bits for each beta value based on QP
bit_num_orient_gamma(): compute the number of bits for each gamma value based on QP
need_correction() : check the orientation, if it is in the edge condition which probably results in a large error, return true; otherwise, return false. compr_insta_separate_data class.

compr_insta_patternID_length : contains a <NUM>-bit unsigned integer indicating the length of the compressed pattern ID of all instances. compr_insta_patternID_data: contains the compressed pattern IDs of all instances. compr_insta_position_length : contains a <NUM>-bit unsigned integer indicating the length of the compressed position of all instances. compr_insta_position_data: contains the compressed positions of all instances. compr_insta_orient_length : contains a <NUM>-bit unsigned integer indicating the length of the compressed orientation of all instances. compr_insta_orient_data: contains the compressed orientation of all instances. compr_insta_scaling_length : contains a <NUM>-bit unsigned integer indicating the length of the compressed scaling factors of all instances. compr_insta_scaling_data: contains the compressed scaling factors of all instances. compr_ins_position_data class.

insta_position_bbox: contains the bounding box of all instance positions. config_n0_symbols : contains n0 ordinary octree configuration symbols. config_n1_symbols : contains n1 ordinary octree configuration symbols. config_n2_symbols : contains n2 ordinary octree configuration symbols. compr_insta_orient_data class.

Additionally, the bitstream described above may also be embedded in other bitstreams such as the SC3DMC bitstream defined by MPEG-3DGC [W11455].

We use the ISO reserved value <NUM> of encodingMode of SC3DMCStreamHeader as follows (Ref. to the original Table <NUM> of [w11455]).

We revise SC3DMCStream defined in [w11455] as follows.

reserved_bits_first : a <NUM> bit unsigned integer which is always <NUM>. It is for byte alignment.

<FIG> depicts a block diagram of an exemplary 3D model encoder <NUM>. The input of apparatus <NUM> may include a 3D model, quality parameter for encoding the 3D model and other metadata. The 3D model first goes through the repetitive structure discovery module <NUM>, which outputs the 3D model in terms of patterns, instances and unique components. A pattern encoder <NUM> is employed to compress the patterns and a unique component encoder <NUM> is employed to encode the unique components. For the instances, the instance component information is encoded based on a user-selected mode. If instance information group mode is selected, the instance information is encoded using grouped instance information encoder <NUM>; otherwise, it is encoded using an elementary instance information encoder <NUM>. The encoded components are further verified in the repetitive structure verifier <NUM>. If an encoded component does not meet its quality requirement, it will be encoded using unique component encoder <NUM>. Bitstreams for patterns, instances, and unique components are assembled at bitstream assembler <NUM>.

<FIG> depicts a block diagram of an exemplary 3D model decoder <NUM>. The input of apparatus <NUM> may include a bitstream of a 3D model, for example, a bitstream generated by encoder <NUM>. The information related to patterns in the compressed bitstream is decoded by pattern decoder <NUM>. Information related to unique components is decoded by unique component decoder <NUM>. The decoding of the instance information also depends on the user-selected mode. If instance information group mode is selected, the instance information is decoded using a grouped instance information decoder <NUM>; otherwise, it is decoded using an elementary instance information decoder <NUM>. The decoded patterns, instance information and unique components are reconstructed to generate an output 3D model at model reconstruction module <NUM>.

The implementations described herein may be implemented in, for example, a method or a process, an apparatus, a software program, a data stream, or a signal. Even if only discussed in the context of a single form of implementation (for example, discussed only as a method), the implementation of features discussed may also be implemented in other forms (for example, an apparatus or program). An apparatus may be implemented in, for example, appropriate hardware, software, and firmware. The methods may be implemented in, for example, an apparatus such as, for example, a processor, which refers to processing devices in general, including, for example, a computer, a microprocessor, an integrated circuit, or a programmable logic device. Processors also include communication devices, such as, for example, computers, cell phones, portable/personal digital assistants ("PDAs"), and other devices that facilitate communication of information between end-users.

As will be evident to one of skill in the art, implementations may produce a variety of signals formatted to carry information that may be, for example, stored or transmitted. The information may include, for example, instructions for performing a method, or data produced by one of the described implementations. For example, a signal may be formatted to carry the bitstream of a described embodiment. Such a signal may be formatted, for example, as an electromagnetic wave (for example, using a radio frequency portion of spectrum) or as a baseband signal. The formatting may include, for example, encoding a data stream and modulating a carrier with the encoded data stream. The information that the signal carries may be, for example, analog or digital information. The signal may be transmitted over a variety of different wired or wireless links, as is known. The signal may be stored on a processor-readable medium.

In principle, the disclosed invention can also be applied to other data compression areas. The invention results in a unique bitstream format.

While the bitstream embeds all the transformation data, it is efficient and may address several applications, where sometimes either bitstream size or decoding efficiency or error resilience matters the most. Therefore, two mode options are disclosed for how to put the transformation data of one instance, i.e. its position, orientation and scaling factor, in the bitstream. In the first mode (Option A), the position, orientation and possible scaling factor of one instance are packed together in the bitstream. In the second mode (Option B), the positions, orientations or possible scaling factors of all instances are packed together in the bitstream.

It will be understood that the present invention has been described purely by way of example, and modifications of detail can be made without departing from the scope of the invention.

Claim 1:
A method for encoding or decoding a bitstream representing a 3D model, comprising:
accessing a plurality of instance information associated with the 3D model, each instance being associated with a corresponding pattern and transformation data; the method being characterized by
encoding, or decoding, the plurality of instance information for transmission in a bitstream in one of first and second modes, wherein the bitstream comprises a header buffer, and wherein in the first mode, the transformation information associated with each respective instance is grouped in the bitstream with the respective instance on an instance by instance basis, and in the second mode, the transformation information is grouped in the bitstream on a transformation information type by transformation type basis;
wherein the header buffer comprises information indicating whether the instance information is grouped in the first mode or in the second mode;
determining whether there is a part of the 3D model not included in any repetitive structure; and
in response to determining that there is a part of the 3D model not included in any repetitive structure, encoding, or decoding, the part of the 3D model not included in any repetitive structure in the bitstream, wherein information in the header buffer is used to indicate whether there is a part of the 3D model not included in any repetitive structure.